https://wiki.net.informatik.uni-goettingen.de/api.php?action=feedcontributions&feedformat=atom&user=Li56 2026-05-16T18:43:52Z User contributions MediaWiki 1.43.8 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2026)&diff=8852 2026-03-08T13:49:55Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Hao Xu[hao.xu@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> |-<br /> | Self-supervised Learning and Foundation Models for Remote Sensing Applications<br /> | In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2025/2026)&diff=8806 2025-10-29T18:39:39Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Dongkuo Wu[dongkuo.wu@cs.uni-goettingen.de];<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''31.01.2026''': Deadline for registration to attend the final presentation<br /> * '''10.02.2026''' : Final Presentations (Offline)<br /> * '''27.02.2026(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Social Media Comments Network Analysis (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Self-supervised Learning and Foundation Models for Remote Sensing Applications<br /> | In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |Service Migration<br /> |When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.<br /> |Edge computing and Machine Learning.<br /> |[yufei.liu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |Task Offloading and Resource Allocation Optimization<br /> |This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.<br /> |Edge computing &amp; Basic optimization algorithms.<br /> |[dongkuo.wu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2025/2026)&diff=8804 2025-10-29T18:39:21Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Dongkuo Wu[dongkuo.wu@cs.uni-goettingen.de];<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''31.01.2026''': Deadline for registration to attend the final presentation<br /> * '''10.02.2026''' : Final Presentations (Offline)<br /> * '''27.02.2026(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Social Media Comments Network Analysis (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Self-supervised Learning and Foundation Models for Remote Sensing Applications<br /> | In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Intelligent Routing<br /> | In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.<br /> | Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.<br /> | [peichen.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |Service Migration<br /> |When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.<br /> |Edge computing and Machine Learning.<br /> |[yufei.liu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |Task Offloading and Resource Allocation Optimization<br /> |This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.<br /> |Edge computing &amp; Basic optimization algorithms.<br /> |[dongkuo.wu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2025/2026)&diff=8802 2025-10-29T18:37:36Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Dongkuo Wu[dongkuo.wu@cs.uni-goettingen.de];<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''31.01.2026''': Deadline for registration to attend the final presentation<br /> * '''10.02.2026''' : Final Presentations (Offline)<br /> * '''27.02.2026(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Social Media Comments Network Analysis (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Self-supervised Learning and Foundation Models for Remote Sensing Applications<br /> | In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Intelligent Routing<br /> | In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.<br /> | Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.<br /> | [peichen.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Resource Optimization in Edge Computing<br /> | This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system. <br /> | Task Scheduling &amp; Caching &amp; Flow Scheduling.<br /> | [Chi Zhang, chi.zhang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |Service Migration<br /> |When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.<br /> |Edge computing and Machine Learning.<br /> |[yufei.liu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |Task Offloading and Resource Allocation Optimization<br /> |This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.<br /> |Edge computing &amp; Basic optimization algorithms.<br /> |[dongkuo.wu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2025)&diff=8748 2025-04-28T09:56:06Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@cs.uni-goettingen.de];<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''03.07.2025''': Deadline for registration to attend the final presentation<br /> * '''20.07.2025''' : Final Presentations (Offline)<br /> * '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Social Media Comments Network Analysis (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Self-supervised Learning and Foundation Models for Remote Sensing Applications<br /> | In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Intelligent Routing<br /> | In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.<br /> | Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.<br /> | [peichen.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Resource Optimization in Edge Computing<br /> | This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system. <br /> | Task Scheduling &amp; Caching &amp; Flow Scheduling.<br /> | [Chi Zhang, chi.zhang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |Service Migration<br /> |When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.<br /> |Edge computing and Machine Learning.<br /> |[yufei.liu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |Task Offloading and Resource Allocation Optimization<br /> |This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.<br /> |Edge computing &amp; Basic optimization algorithms.<br /> |[dongkuo.wu@cs.uni-goettingen.de]<br /> |<br /> |Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2025)&diff=8684 2025-03-12T15:35:49Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details?sem_id=b2a06d639bd9b2128c3fffb4a03bd163]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 07.04<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 14.04<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 28.04<br /> | Lecture 3 3D Object Detection II <br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 4 3D Object Detection III &amp; Introduction of Hardware, Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Introduction of data sampling, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 5-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | <br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | <br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Aug. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2025)&diff=8682 2025-03-12T15:34:31Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |module=M.Inf.1124<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@cs.uni-goettingen.de];<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''03.07.2025''': Deadline for registration to attend the final presentation<br /> * '''20.07.2025''' : Final Presentations (Offline)<br /> * '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Remote Sensing Image Registration<br /> | In this topic, you will study and apply methods for the registration of multimodal remote sensing images with different resolution.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Intelligent Routing<br /> | In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.<br /> | Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.<br /> | [peichen.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Resource Optimization in Edge Computing<br /> | This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system. <br /> | Task Scheduling &amp; Caching &amp; Flow Scheduling.<br /> | [Chi Zhang, chi.zhang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2025)&diff=8638 2025-02-25T15:00:54Z

<p>Li56: Created page with &quot;{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180h, 6 ECTS |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li] |ta=Zhengze Li, Yanlong Huang |time=Mondays 10:00 - 12:00am |place...&quot;</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details?sem_id=b2a06d639bd9b2128c3fffb4a03bd163]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 3 3D Object Detection II <br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 4 3D Object Detection III &amp; Introduction of Hardware, Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Introduction of data sampling, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 5-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | <br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | <br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | <br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Aug. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2024/2025)&diff=8584 2024-10-28T10:42:33Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details?sem_id=b2a06d639bd9b2128c3fffb4a03bd163]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 21.10.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 28.10.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 04.11.2024<br /> | Lecture 3 3D Object Detection II <br /> |-<br /> | align=&quot;left&quot; | 11.11.2024<br /> | Lecture 4 3D Object Detection III &amp; Introduction of Hardware, Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 18.11.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 25.11.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 02.12.2024-09.12.2024<br /> | Lecture 5-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 16.12.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 06.01.2025<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 13.01.2025<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.01.2025<br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 03.02.2025<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Mar. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2024/2025)&diff=8582 2024-10-25T11:58:05Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tong Shen[shen.tong@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2025''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2025''' : Final Presentations (Offline)<br /> * '''TBD.02.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, student will study how AI models / GNNs are explained by SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Anomaly Detection in Graphs<br /> | In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Influence of LLM robots in social networks (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Emotional Support Conversation Generation <br /> | Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.<br /> | Large Language Model &amp; Emotional Support<br /> | [Jing Li, jing.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Intelligent Routing<br /> | In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.<br /> | Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.<br /> | [peichen.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Rumor control and detection<br /> | This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.<br /> | Information Propagation &amp; GCN.<br /> | [Fei Gao, fei.gao@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Resource Optimization in Edge Computing<br /> | This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system. <br /> | Task Scheduling &amp; Caching &amp; Flow Scheduling.<br /> | [Chi Zhang, chi.zhang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present your topic to the audience (in English).<br /> * The final presentation will be conducted '''offline'''.<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2024/2025)&diff=8570 2024-10-22T13:06:43Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details?sem_id=b2a06d639bd9b2128c3fffb4a03bd163]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 21.10.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 28.10.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 04.11.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 11.11.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 18.11.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 25.11.2024-09.12.2024<br /> | Lecture 4-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 16.12.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 06.01.2025<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 13.01.2025<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.01.2025<br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 03.02.2025<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Mar. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2024/2025)&diff=8568 2024-10-22T13:02:09Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e1s1]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 21.10.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 28.10.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 04.11.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 11.11.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 18.11.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 25.11.2024-09.12.2024<br /> | Lecture 4-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 16.12.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 06.01.2025<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 13.01.2025<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.01.2025<br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 03.02.2025<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Mar. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2024/2025)&diff=8516 2024-09-06T10:20:01Z

<p>Li56: Created page with &quot;{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180h, 6 ECTS |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li] |ta=Zhengze Li, Yanlong Huang |time=Mondays 10:00 - 12:00am |place...&quot;</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 21.10.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 28.10.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 04.11.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 11.11.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 18.11.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 25.11.2024-09.12.2024<br /> | Lecture 4-6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 16.12.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 06.01.2025<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 13.01.2025<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.01.2025<br /> | Intermediate meeting Task 3<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 03.02.2025<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD (Mar. 2025)<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2024)&diff=8496 2024-05-23T09:26:47Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tong Shen[shen.tong@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''03.07.2024''': Deadline for registration to attend the final presentation<br /> * '''20.07.2024''' : Final Presentations (Online, wait to decide)<br /> * '''30.08.2024 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Biomass estimation from Satellite Images<br /> | In this topic, you will study methods to estimate the biomass of trees from satellite images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, student will study how AI models / GNNs are explained by SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Anomaly Detection in Graphs<br /> | In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Influence of LLM robots in social networks (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2024)&diff=8470 2024-04-09T12:00:20Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tong Shen[shen.tong@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''03.07.2024''': Deadline for registration to attend the final presentation<br /> * '''20.07.2024''' : Final Presentations (Online, wait to decide)<br /> * '''30.08.2024 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Disaster Monitoring<br /> | In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Biomass estimation from Satellite Images<br /> | In this topic, you will study methods to estimate the biomass of trees from satellite images.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, student will study how AI models / GNNs are explained by SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Anomaly Detection in Graphs<br /> | In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The relationship between birds’ distribution and the health of the environment (Project possible)<br /> | Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.<br /> | Basic Python knowledge, correlation analysis<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Influence of LLM robots in social networks (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Knowledge Graph Completion <br /> | What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.<br /> | Knowledge Graph &amp; NLP<br /> | [Tong Shen, shen.tong@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2024)&diff=8400 2024-04-02T13:32:01Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Mondays 10:00 - 12:00am<br /> |place= [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 08.04.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 15.04.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 22.04.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 29.04.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 06.05.2024<br /> | No lecture (Task 1)<br /> |-<br /> | align=&quot;left&quot; | 13.05.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.05.2024<br /> | No Lecture (Pfingstmontag)<br /> |-<br /> | align=&quot;left&quot; | 27.05.2024<br /> | Lecture 4 Testbed configuration &amp; Data Sampling (by group)<br /> |- <br /> | align=&quot;left&quot; | 03.06.2024<br /> | Lecture 5 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 10.06.2024<br /> | Lecture 6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 17.06.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 24.06.2024<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 01.07.2024<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 08.07.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 15.07.2024<br /> | No Lecture (Task 3)<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 22.07.2024<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2024)&diff=8392 2024-03-13T14:55:20Z

<p>Li56: /* Grading */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 08.04.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 15.04.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 22.04.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 29.04.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 06.05.2024<br /> | No lecture (Task 1)<br /> |-<br /> | align=&quot;left&quot; | 13.05.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.05.2024<br /> | No Lecture (Pfingstmontag)<br /> |-<br /> | align=&quot;left&quot; | 27.05.2024<br /> | Lecture 4 Testbed configuration &amp; Data Sampling (by group)<br /> |- <br /> | align=&quot;left&quot; | 03.06.2024<br /> | Lecture 5 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 10.06.2024<br /> | Lecture 6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 17.06.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 24.06.2024<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 01.07.2024<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 08.07.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 15.07.2024<br /> | No Lecture (Task 3)<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 22.07.2024<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1/2/3: 20% each<br /> ** Presentation: 20%<br /> ** Report&amp;Code: 20%<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2024)&diff=8390 2024-03-13T14:52:26Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 08.04.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 15.04.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 22.04.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 29.04.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 06.05.2024<br /> | No lecture (Task 1)<br /> |-<br /> | align=&quot;left&quot; | 13.05.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.05.2024<br /> | No Lecture (Pfingstmontag)<br /> |-<br /> | align=&quot;left&quot; | 27.05.2024<br /> | Lecture 4 Testbed configuration &amp; Data Sampling (by group)<br /> |- <br /> | align=&quot;left&quot; | 03.06.2024<br /> | Lecture 5 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 10.06.2024<br /> | Lecture 6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 17.06.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 24.06.2024<br /> | Lecture 7 Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 01.07.2024<br /> | Lecture 8 GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 08.07.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 15.07.2024<br /> | No Lecture (Task 3)<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 22.07.2024<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2024)&diff=8388 2024-03-13T14:51:31Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 08.04.2024<br /> | Lecture 1 Introduction &amp; Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 15.04.2024<br /> | Lecture 2 3D Object Detection I<br /> |-<br /> | align=&quot;left&quot; | 22.04.2024<br /> | Lecture 3 3D Object Detection II &amp; Introduction of Hardware, Release of Task 1<br /> |- <br /> | align=&quot;left&quot; | 29.04.2024<br /> | Intermediate meeting of Task 1<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 06.05.2024<br /> | No lecture (Task 1)<br /> |-<br /> | align=&quot;left&quot; | 13.05.2024<br /> | Introduction of data sampling, Task 1 report submission (Before 10PM), Release of Task 2<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 20.05.2024<br /> | No Lecture (Pfingstmontag)<br /> |-<br /> | align=&quot;left&quot; | 27.05.2024<br /> | Lecture 4 Testbed configuration &amp; Data Sampling (by group)<br /> |- <br /> | align=&quot;left&quot; | 03.06.2024<br /> | Lecture 5 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 10.06.2024<br /> | Lecture 6 Testbed configuration &amp; Data Sampling (by group)<br /> |-<br /> | align=&quot;left&quot; | 17.06.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 24.06.2024<br /> | Python Stacks<br /> |-<br /> | align=&quot;left&quot; | 01.07.2024<br /> | GNN, Release of Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 08.07.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> |-<br /> | align=&quot;left&quot; | 15.07.2024<br /> | No Lecture (Task 3)<br /> |-<br /> |- <br /> | align=&quot;left&quot; | 22.07.2024<br /> | No Lecture, Task 3 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> |}<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=AI-Empowered_Networking_and_Mobile_Communications(Summer_2024)&diff=8361 2024-02-28T12:16:18Z

<p>Li56: /* Schedule (Tentative) */</p> <hr /> <div><br /> == Details ==<br /> {{CourseDetails<br /> |credits=5ECTS<br /> |module= M.Inf.1223.Mp: Advanced Topics in Computer Networks<br /> B.Inf.1702.Mp: Vertiefung Computersysteme<br /> <br /> M.Inf.1120.Mp: Mobilkommunikation<br /> <br /> M.Inf.121.1: Mobilkommunikation I<br /> <br /> M.Inf.225.Mp: Ausgewählte Themen der Mobilkommunikation<br /> <br /> Note: You can choose any of them to attend this course, but only one! Please note that enrolling in the same course more than once will not grant additional credits.<br /> |lecturer=Dr. Tingting Yuan, [http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]<br /> |ta= Fabian Wölk<br /> |time=Thursdays, 10-12am.<br /> |place=IFI 2.101<br /> |univz=[]<br /> }}<br /> <br /> ==Course description==<br /> This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to: <br /> *Software-defined Networking (?)<br /> *Segment Routing<br /> *Video Analysis in Edge Networks (?)<br /> *Information Centric Network<br /> *Big Data and Social Network<br /> <br /> For each topic, basic structures, features and applied techniques will be taught.<br /> <br /> If you have any questions, please contact Fabian Wölk (fabian.woelk@cs.uni-goettingen.de)<br /> <br /> ==Schedule (Tentative)==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''Date'''<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Lecturer'''<br /> |{{Hl2}} |'''Slides'''<br /> |-<br /> | align=&quot;right&quot; | 11.04.2024 (10:00-12:00am)<br /> | Introduction <br /> | Dr. Tingting Yuan<br /> | <br /> |-<br /> | align=&quot;right&quot; | 18.04.2024 (10:00-12:00am)<br /> | AI meets Networking I<br /> | Dr. Tingting Yuan<br /> | <br /> |-<br /> | align=&quot;right&quot; | 25.04.2024<br /> | NO LECTURE (GIRL'S DAY)<br /> | <br /> |-<br /> | align=&quot;right&quot; | 02.05.2024 (10:00-12:00am)<br /> | AI meets Networking II<br /> | Dr. Tingting Yuan<br /> | <br /> |-<br /> | align=&quot;right&quot; | 09.05.2024<br /> | NO LECTURE (PUBLIC HOLIDAY)<br /> | <br /> | <br /> |-<br /> | align=&quot;right&quot; | 16.05.2024 (10:00-12:00am)<br /> | Segment Routing I<br /> | Fabian Wölk<br /> | <br /> |-<br /> | align=&quot;right&quot; | 23.05.2024 (10:00-12:00am)<br /> | Segment Routing II<br /> | Fabian Wölk<br /> | <br /> |-<br /> | align=&quot;right&quot; | 30.05.2024 (10:00-12:00am)<br /> | Intelligent Transportation Application based on V2I Networking<br /> | Yanlong Huang<br /> | <br /> |-<br /> | align=&quot;right&quot; | 06.06.2024 (10:00-12:00am)<br /> | Social Network Analysis<br /> | Zhengze Li<br /> | <br /> |-<br /> | align=&quot;right&quot; | 13.06.2024 (10:00-12:00am)<br /> | From Words to Vision: A Journey Through Multimodal Sentiment Analysis ?<br /> | Wenfang Wu<br /> | <br /> |-<br /> | align=&quot;right&quot; | 20.06.2024 (10:00-12:00am)<br /> | ?<br /> | Jin<br /> | <br /> |-<br /> | align=&quot;right&quot; | 27.06.2024 (10:00-12:00am)<br /> | ?<br /> | Parisa<br /> | <br /> |-<br /> | align=&quot;right&quot; | 04.07.2024 (10:00-12:00am)<br /> | ?<br /> | Tong<br /> | <br /> |-<br /> | align=&quot;right&quot; | 11.07.2024 (10:00-12:00am)<br /> | ?<br /> | Yanru<br /> | <br /> |-<br /> | align=&quot;right&quot; | TBD (12:00-14:00am)<br /> | Written Examination (Room TBD)<br /> | <br /> |<br /> |-<br /> |}<br /> <br /> ==Prerequisites==<br /> * Computer Science I, II; Computer Networks<br /> <br /> ==References===<br /> * Yang, S., N. He, F. Li, and X. Fu, Resource Allocation in Network Function Virtualization: Problems, Models and Algorithms, Singapore: Springer, August 2022.<br /> <br /> * James Kurose, Keith Ross, Computer Networking: A Top-Down Approach. 8th Edition, Pearson, June 2021<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2024)&diff=8359 2024-02-28T12:14:57Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Dr. Tingting Yuan [tingting.yuan@informatik.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''03.07.2024''': Deadline for registration to attend the final presentation<br /> * '''20.07.2024''' : Final Presentations (Online, wait to decide)<br /> * '''30.08.2024 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> <br /> |-<br /> |OCR (Optical Character Recognition) and Annotation Transfer<br /> | The aim of the project is to develop a tool/software that can convert a printed paper with annotations and text into electronic versions with text highlighting and annotations<br /> | Basic machine learning knowledge<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Open topics<br /> | Open topics in Data Science &amp; Applied Statistics, especially XAI<br /> | Depends<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based pedestrian distribution monitoring <br /> | In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based traffic usage analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2024)&diff=8357 2024-02-28T12:02:30Z

<p>Li56: Created page with &quot;{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180...&quot;</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> TBD (will be published before 20. March)<br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8287 2023-11-03T10:11:52Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1 The Data Science Pipeline<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 Python Stack &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 06.11.2023<br /> | Lecture 3 GNN<br /> |- <br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 4 Video Analytics<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 5 Video Analytics &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8285 2023-11-03T10:08:01Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 06.11.2023<br /> | Lecture 3<br /> |- <br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 4<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 5 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8277 2023-11-01T14:40:53Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, student will study how AI models / GNNs are explained by SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Anomaly Detection in Graphs<br /> | In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The relationship between birds’ distribution and the health of the environment (Project possible)<br /> | Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.<br /> | Basic Python knowledge, correlation analysis<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Influence of LLM robots in social networks (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8273 2023-10-31T21:57:18Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, student will study how AI models / GNNs are explained by SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Anomaly Detection in Graphs<br /> | In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Influence of LLM robots in social networks (Intern/Project/Thesis possible)<br /> | In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8271 2023-10-31T10:17:53Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8265 2023-10-25T12:45:30Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8261 2023-10-23T08:08:56Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | No<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2023/2024)&diff=8255 2023-10-16T15:16:20Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Wanghong Yang [wanghong.yang@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''TBD.01.2024''': Deadline for registration to attend the final presentation<br /> * '''TBD.01.2024''' : Final Presentations (Online, wait to decide)<br /> * '''TBD.02.2024(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | How to do efficient offline training<br /> | In this topic, you will study how to do efficient offline training for reinforcement learning<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Lidar-based traffic flow analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic point cloud processing &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Multimodal Large Language Model Evaluation for Multimodal Tasks<br /> | This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.<br /> | Large Language Model &amp; multimodal setting<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Low latency transmission mechanism for real-time interactive application <br /> | There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to &quot;negative optimization&quot;. How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.<br /> | Network Transmission Improvement &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8245 2023-10-14T21:37:29Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 06.11.2023<br /> | Lecture 3<br /> |- <br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 4<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 5 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8243 2023-10-14T21:36:50Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 3<br /> |- <br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 4<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 5 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8241 2023-10-14T21:19:33Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 3 <br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8239 2023-10-14T21:14:44Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 23.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 2 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 13.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 27.11.2023<br /> | No Lecture, Task 1 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Lecture 3 <br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | No Lecture, Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8217 2023-09-26T14:01:52Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 06.11.2023<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 13.11.2023<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 20.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 22.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 01.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 04.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2023/2024)&diff=8215 2023-09-26T13:55:23Z

<p>Li56: </p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 30.10.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 06.11.2023<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 13.11.2023<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 20.11.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 04.12.2023<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 11.12.2023<br /> | No Lecture (Whit Monday)<br /> |-<br /> | align=&quot;left&quot; | 18.12.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 08.01.2024<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 15.01.2024<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 22.01.2024<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 29.01.2024<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 08.03.2024<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 11.03.2024<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2023)&diff=8167 2023-05-09T11:29:01Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 17.04.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 24.04.2023<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 01.05.2023<br /> | No Lecture (Labour Day)<br /> |-<br /> | align=&quot;left&quot; | 08.05.2023<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 15.05.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 22.05.2023<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 29.05.2023<br /> | No Lecture (Whit Monday)<br /> |-<br /> | align=&quot;left&quot; | 05.06.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 12.06.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.06.2023<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.06.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 03.07.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 04.08.2023<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 07.08.2023<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2023)&diff=8157 2023-04-24T20:14:04Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Dr. Tingting Yuan [tingting.yuan@informatik.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''01.09.2023''': Deadline for registration to attend the final presentation<br /> * '''08.09.2023''' : Final Presentations (Online)<br /> * '''30.09.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> <br /> |-<br /> |OCR (Optical Character Recognition) and Annotation Transfer<br /> | The aim of the project is to develop a tool/software that can convert a printed paper with annotations and text into electronic versions with text highlighting and annotations<br /> | Basic machine learning knowledge<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Satellite Image Pixel Clustering for Change Estimation<br /> | In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Open topics<br /> | Open topics in Data Science &amp; Applied Statistics, especially XAI<br /> | Depends<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based pedestrian distribution monitoring <br /> | In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based traffic usage analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Personalized chatbot based on ChatGPT <br /> | In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.<br /> | NLP &amp; ChatGPT<br /> | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Performance of real 5G communication <br /> | What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.<br /> | Network protocol stack &amp; 5G architecture<br /> | [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2023)&diff=8085 2023-03-27T13:39:57Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Weijun Wang [weijun.wang@informatik.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''01.09.2023''': Deadline for registration to attend the final presentation<br /> * '''08.09.2023''' : Final Presentations (Online)<br /> * '''30.09.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Privacy protection in video analytics<br /> | In this topic, you will study how to do privacy protection in video analytics, e.g., video blur<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Satellite-based approaches for Flood Management<br /> | In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Python(Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)<br /> | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.<br /> | Python &amp; CV knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Open topics<br /> | Open topics in Data Science &amp; Applied Statistics, especially XAI<br /> | Depends<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based pedestrian distribution monitoring <br /> | In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Vision-based traffic usage analysis <br /> | In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.<br /> | Basic CV &amp; ML knowledge<br /> | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Summer_2023)&diff=8047 2023-03-08T12:28:14Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Weijun Wang [weijun.wang@informatik.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''01.09.2023''': Deadline for registration to attend the final presentation<br /> * '''08.09.2023''' : Final Presentations (Online)<br /> * '''30.09.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Network management with deep reinforcement learning<br /> | In this topic, you will study deep reinforcement learning used in network management, e.g., traffic congestion control, and adaptive bitrate streaming.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Satellite-based approaches for Flood Management<br /> | In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Explainable AI(XAI) / graph neural network (XGNN)<br /> | In this topic, students study how AI models / GNNs are explained with SOTA papers.<br /> | Basic AI / GNN knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network (Intern/Project/Thesis possible)<br /> | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.<br /> | Python skills (Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | The life-circle of vanished scientific journals (Intern/Project/Thesis possible)<br /> | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.<br /> | Python skills (Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Traffic prediction with GNN (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use XGNN to predict traffic volumn.<br /> | Strong Python skills (Modeling and Visualization). Graph and XAI knowledge.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)<br /> | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.<br /> | Strong Python skills (Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> | Open topics<br /> | Open topics in Data Science &amp; Applied Statistics, especially XAI<br /> | Depends<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> | <br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2023)&diff=8021 2023-02-09T13:30:52Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 17.04.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 24.04.2023<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 01.05.2023<br /> | No Lecture (Labour Day)<br /> |-<br /> | align=&quot;left&quot; | 08.05.2023<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 15.05.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 22.05.2023<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 29.05.2023<br /> | No Lecture (Whit Monday)<br /> |-<br /> | align=&quot;left&quot; | 05.06.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 12.06.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.06.2023<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.06.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 03.07.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final report submission<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Summer_2023)&diff=8001 2023-02-02T12:36:20Z

<p>Li56: Created page with &quot;{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180...&quot;</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Yanlong Huang<br /> |time=Monday 10:00 - 12:00am<br /> |place= TBD<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 17.04.2023<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 24.04.2023<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 01.05.2023<br /> | No Lecture (Labour Day)<br /> |-<br /> | align=&quot;left&quot; | 08.05.2023<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 15.05.2023<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 22.05.2023<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 29.05.2023<br /> | No Lecture (Whit Monday)<br /> |-<br /> | align=&quot;left&quot; | 05.06.2023<br /> | Lecture 4 &amp; Release of Task 2 <br /> |-<br /> | align=&quot;left&quot; | 12.06.2023<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.06.2023<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.06.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 03.07.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final Presentation<br /> |-<br /> | align=&quot;left&quot; | TBD<br /> | Final report submission<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7969 2023-01-30T10:42:58Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | No Lecture (Reformationstag)<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 14.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 21.11.2022<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 28.11.2022<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 05.12.2022<br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 12.12.2022<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.12.2022<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.12.2022<br /> | No Lecture. (2. Weihnachtsfeiertag)<br /> |-<br /> | align=&quot;left&quot; | 02.01.2023<br /> | No Lecture. (Vorlesungsfreie Zeit)<br /> |-<br /> | align=&quot;left&quot; | 09.01.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 30.01.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 17.02.2023<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 21.02.2023<br /> | Final Presentation<br /> |-<br /> | align=&quot;left&quot; | 27.02.2023<br /> | Final report submission<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7957 2023-01-23T22:32:39Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | No Lecture (Reformationstag)<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 14.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 21.11.2022<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 28.11.2022<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 05.12.2022<br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 12.12.2022<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.12.2022<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.12.2022<br /> | No Lecture. (2. Weihnachtsfeiertag)<br /> |-<br /> | align=&quot;left&quot; | 02.01.2023<br /> | No Lecture. (Vorlesungsfreie Zeit)<br /> |-<br /> | align=&quot;left&quot; | 09.01.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 30.01.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | 17.02.2023<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | 21.02.2023<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2022/2023)&diff=7899 2022-11-24T14:20:58Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''24.12.2022 (wait to make sure) ''': Deadline for registration to attend the final presentation<br /> * '''01.2023''' : Final Presentations (IFI 1.101)<br /> * '''15.02.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Network management with deep reinforcement learning<br /> | In this topic, you will study deep reinforcement learning used in network management, e.g., traffic congestion control, and adaptive bitrate streaming.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Satellite-based approaches for Flood Management<br /> | In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network<br /> | In this topic, you will study methods to crawl the dataset from social networks and utilize social science network analysis in any topic you are interested in (science/education/politics…) to find out the network structure and compare the difference among different topics.<br /> | Basic programming knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Analysis of MOOC Discussion Forum<br /> | In this topic you will study methods to crawl the dataset from MOOCs and evaluate if the active users have more influence on overall forum activities and the evaluation of the course.<br /> | Basic programming knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Seminar_on_Internet_Technologies_(Winter_2022/2023)&diff=7875 2022-11-04T09:17:22Z

<p>Li56: /* Topics */</p> <hr /> <div>== Details ==<br /> <br /> <br /> {{CourseDetails<br /> |credits=5 ECTS (BSc/MSc AI); 5 (ITIS)<br /> |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]<br /> |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];<br /> |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> |ta = Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]<br /> |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''<br /> }}<br /> <br /> ==Announcement==<br /> No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.<br /> <br /> ==Course description==<br /> <br /> This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.<br /> <br /> The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.<br /> <br /> Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.<br /> <br /> Note: Participants in the seminar only need to register for the exam before the end of the course.<br /> <br /> ==Passing requirements==<br /> *There will be 2 milestones before the presentations that the students should pass before they register for the course.<br /> **Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.<br /> **Midterm milestone. (ex. programming tasks are done etc... ) <br /> <br /> *Actively and frequently participate in the project communication with the topic advisor<br /> **This accounts for 20% of your grade.<br /> * Present the selected topic (20 min. presentations + 10 min. Q&amp;A).<br /> ** This accounts for 40% of your grade.<br /> * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).<br /> ** This accounts for 40% of your grade.<br /> * Please check the [[#Schedule]] and adhere to it.<br /> <br /> ==Schedule==<br /> * '''24.12.2022 (wait to make sure) ''': Deadline for registration to attend the final presentation<br /> * '''01.2023''' : Final Presentations (IFI 1.101)<br /> * '''15.02.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).<br /> <br /> == Topics ==<br /> <br /> {| align=&quot;center&quot; class=&quot;wikitable sortable&quot; {{Prettytable}} <br /> |-<br /> |{{Hl2}} |'''Topic'''<br /> |{{Hl2}} |'''Description'''<br /> |{{Hl2}} |'''Prerequisites'''<br /> |{{Hl2}} |'''Topic Advisor'''<br /> |{{Hl2}} |'''Readings'''<br /> |{{Hl2}} |'''Available'''<br /> |-<br /> | Network management with deep reinforcement learning<br /> | In this topic, you will study deep reinforcement learning used in network management, e.g., traffic congestion control, and adaptive bitrate streaming.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> |AI painter<br /> | In this topic, you will study how AI has been used for painting. e.g. GAN.<br /> | Basic programming knowledge, Basic machine learning knowledge, need coding work<br /> | [Tingting Yuan, tingt.yuan@hotmail.com]<br /> |[https://topten.ai/ai-painting-generators/]<br /> | Yes<br /> |-<br /> | Change Detection in Satellite Image Time Series<br /> | In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Satellite-based approaches for Flood Management<br /> | In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.<br /> | Basic machine learning knowledge<br /> | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Social Media Comments Network<br /> | In this topic, you will study methods to crawl the dataset from social networks and utilize social science network analysis in any topic you are interested in (science/education/politics…) to find out the network structure and compare the difference among different topics.<br /> | Basic programming knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> | Analysis of MOOC Discussion Forum<br /> | In this topic you will study methods to crawl the dataset from MOOCs and evaluate if the active users have more influence on overall forum activities and the evaluation of the course.<br /> | Basic programming knowledge<br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | No<br /> |-<br /> |-<br /> | Open topics<br /> | Topics regarding to computer science<br /> | <br /> | [Zhengze Li, zhengze.li@cs.uni-goettingen.de]<br /> |<br /> | Yes<br /> |-<br /> |-<br /> |}<br /> <br /> ==Workflow==<br /> <br /> === 1. Select a topic ===<br /> Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.<br /> <br /> === 2. Get your work advised ===<br /> Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. <br /> It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.<br /> <br /> === 3. Approach your topic ===<br /> <br /> * By choosing a topic, you will get a direction of elaboration.<br /> * You may work in different styles, for example:<br /> ** Survey: Basic introduction, an overview of the field; general problems, methods, approaches.<br /> ** Specific problem: Detailed introduction, details about the problem, and the solution.<br /> * Based on the research, you should have your own ideas on your topic.<br /> <br /> === 4. Prepare presentation ===<br /> <br /> * Present on your topic to the audience (in English).<br /> * 20 minutes of presentation followed by 10 minutes of discussion.<br /> <br /> You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.<br /> <br /> Hints for preparing the presentation:<br /> If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.<br /> Please make sure to finish your presentation in time.<br /> <br /> Suggestions for preparing the slides:<br /> No more than 20 pages/slides.<br /> Get your audiences to quickly understand the general idea.<br /> Figures, tables, and animations are better than sentences.<br /> Don't forget a summary of the topic and your ideas.<br /> <br /> === 5. Write a report ===<br /> <br /> * Present the problem with its background.<br /> * Detail the approaches, techniques, and methods to solve the problem.<br /> * Evaluate and assess those approaches (e.g., pros and cons).<br /> * Give a short outlook on potential future developments.<br /> <br /> The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).<br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.<br /> <br /> === 6. Course schedule===<br /> There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.<br /> <br /> [[Category:Courses]]</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7843 2022-10-21T18:42:23Z

<p>Li56: /* Details */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://ecampus.zvw.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&amp;_flowExecutionKey=e5s12]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | No Lecture (Reformationstag)<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 14.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 21.11.2022<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 28.11.2022<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 05.12.2022<br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 12.12.2022<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.12.2022<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.12.2022<br /> | No Lecture. (2. Weihnachtsfeiertag)<br /> |-<br /> | align=&quot;left&quot; | 02.01.2023<br /> | No Lecture. (Vorlesungsfreie Zeit)<br /> |-<br /> | align=&quot;left&quot; | 09.01.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 23.01.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7841 2022-10-21T18:33:03Z

<p>Li56: /* Course Organization */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A8B3DFB635EA200C7E9420D0B180F622.s45?state=verpublish&amp;status=init&amp;vmfile=no&amp;publishid=302542&amp;moduleCall=webInfo&amp;publishConfFile=webInfo&amp;publishSubDir=veranstaltung]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | No Lecture (Reformationstag)<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 14.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 21.11.2022<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 28.11.2022<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 05.12.2022<br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 12.12.2022<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.12.2022<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.12.2022<br /> | No Lecture. (2. Weihnachtsfeiertag)<br /> |-<br /> | align=&quot;left&quot; | 02.01.2023<br /> | No Lecture. (Vorlesungsfreie Zeit)<br /> |-<br /> | align=&quot;left&quot; | 09.01.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 23.01.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7839 2022-10-21T16:41:29Z

<p>Li56: /* Schedule */</p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A8B3DFB635EA200C7E9420D0B180F622.s45?state=verpublish&amp;status=init&amp;vmfile=no&amp;publishid=302542&amp;moduleCall=webInfo&amp;publishConfFile=webInfo&amp;publishSubDir=veranstaltung]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | No Lecture (Reformationstag)<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 14.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | 21.11.2022<br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | 28.11.2022<br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | 05.12.2022<br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | 12.12.2022<br /> | Intermediate meeting of Task 2<br /> |-<br /> | align=&quot;left&quot; | 19.12.2022<br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | 26.12.2022<br /> | No Lecture. (2. Weihnachtsfeiertag)<br /> |-<br /> | align=&quot;left&quot; | 02.01.2023<br /> | No Lecture. (Vorlesungsfreie Zeit)<br /> |-<br /> | align=&quot;left&quot; | 09.01.2023<br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | 23.01.2023<br /> | Intermediate meeting Task 3<br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; | TBD (In Feb.)<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56 https://wiki.net.informatik.uni-goettingen.de/index.php?title=Data_Science_in_Smart_City_(Winter_2022/2023)&diff=7825 2022-10-16T19:24:23Z

<p>Li56: </p> <hr /> <div>{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}<br /> <br /> <br /> == Details ==<br /> {{CourseDetails<br /> |credits=180h, 6 ECTS<br /> |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke<br /> |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/zhengze-li Zhengze Li]<br /> |ta=Zhengze Li, Bowen Li<br /> |time=Monday 10:00 - 12:00am<br /> |place= IFI 0.101<br /> |univz=[https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A8B3DFB635EA200C7E9420D0B180F622.s45?state=verpublish&amp;status=init&amp;vmfile=no&amp;publishid=302542&amp;moduleCall=webInfo&amp;publishConfFile=webInfo&amp;publishSubDir=veranstaltung]<br /> }}<br /> <br /> ==Course Organization==<br /> In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. <br /> <br /> While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:<br /> <br /> * Introduction to the practical data science pipeline<br /> * Exploratory data analysis<br /> * The Python Data Science stack<br /> * Video Analytics<br /> * Advanced algorithms for Data Science<br /> * Parameter tuning for predictive models<br /> <br /> The goal of this course is to:<br /> <br /> * Help students to further understand computer networks and data science knowledge.<br /> * Help students to use computer science knowledge to build a practical AI system.<br /> * Guide students to utilize knowledge to improve the performance of the system.<br /> <br /> In this course, each student (max. number 30) needs to:<br /> <br /> * Read state-of-art papers.<br /> * Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.<br /> * Learn how to analyze city public transport sensor data.<br /> <br /> Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.<br /> A final report needs to be submitted at the end of the semester.<br /> <br /> ==Prerequisites==<br /> *You are ''highly recommended'' to have completed a course on Data Science (e.g., &quot;[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics&quot; taught by Dr. Steffen Herbold] or the Course &quot;Machine Learning&quot; by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.<br /> *Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries<br /> <br /> ==Schedule==<br /> {| {{Prettytable|width=}}<br /> |-<br /> |{{Hl2}} |'''When?'''<br /> |{{Hl2}} |'''What?'''<br /> |-<br /> | align=&quot;left&quot; | 24.10.2022<br /> | Lecture 1<br /> |-<br /> | align=&quot;left&quot; | 31.10.2022<br /> | Lecture 2<br /> |-<br /> | align=&quot;left&quot; | 07.11.2022<br /> | Lecture 3 &amp; Release of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting of Task 1<br /> |-<br /> | align=&quot;left&quot; | <br /> | Task 1 report submission (Before 10PM)<br /> |- <br /> | align=&quot;left&quot; | <br /> | Lecture 4 &amp; Release of Task 2<br /> |-<br /> | align=&quot;left&quot; | <br /> | Task 2 report submission (Before 10PM)<br /> |-<br /> | align=&quot;left&quot; | <br /> | Release of Task 3<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting 1 of Task 3<br /> |-<br /> | align=&quot;left&quot; | <br /> | Intermediate meeting 2 of Task 3<br /> |-<br /> | align=&quot;left&quot; | <br /> | Report Submitting <br /> |-<br /> | align=&quot;left&quot; |<br /> | Final Presentation<br /> |-<br /> |}<br /> <br /> '''Where?''': [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&amp;status=init&amp;vmfile=no&amp;moduleCall=webInfo&amp;publishConfFile=webInfoRaum&amp;publishSubDir=raum&amp;keep=y&amp;raum.rgid=8903 Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]<br /> <br /> ==Grading==<br /> ** Task 1: 25%<br /> ** Task 2: 25%<br /> ** Task 3: 50% (Presentation: 20%, Report&amp;Code: 30%)<br /> <br /> * Presentation: <br /> **Present on your work with a slide to the audience (in English).<br /> **20 minutes of presentation followed by 10 minutes Q&amp;A.<br /> Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions. <br /> All quoted images, tables and text need to indicate their source.<br /> Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. <br /> <br /> * Final report: <br /> The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). <br /> Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.<br /> The source code, data (or URL of data) and a manual should be uploaded with the report.</div>

Li56