NET Wiki - User contributions [en]

Seminar on Internet Technologies (Summer 2023)

2023-03-08T10:52:27Z

Wwang: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Weijun Wang [weijun.wang@informatik.uni-goettingen.de]
|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.'''
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

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.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

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.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''01.09.2023''': Deadline for registration to attend the final presentation
* '''08.09.2023''' : Final Presentations (Online)
* '''30.09.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Network management with deep reinforcement learning
| In this topic, you will study deep reinforcement learning used in network management, e.g., traffic congestion control, and adaptive bitrate streaming.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| No
|-
|-
|AI painter
| In this topic, you will study how AI has been used for painting. e.g. GAN.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://topten.ai/ai-painting-generators/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Satellite-based approaches for Flood Management
| In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network
| 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.
| Basic programming knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Analysis of MOOC Discussion Forum
| 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.
| Basic programming knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
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.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
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.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes of discussion.

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.

Hints for preparing the presentation:
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.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
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.

=== 6. Course schedule===
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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2023)

2023-03-08T10:48:09Z

Wwang: /* Details */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Weijun Wang [weijun.wang@informatik.uni-goettingen.de]
|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.'''
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

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.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

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.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''23.01.2023''': Deadline for registration to attend the final presentation
* '''02.02.2023''' : Final Presentations (IFI 0.101)
* '''24.02.2023 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Network management with deep reinforcement learning
| In this topic, you will study deep reinforcement learning used in network management, e.g., traffic congestion control, and adaptive bitrate streaming.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| No
|-
|-
|AI painter
| In this topic, you will study how AI has been used for painting. e.g. GAN.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://topten.ai/ai-painting-generators/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Satellite-based approaches for Flood Management
| In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network
| 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.
| Basic programming knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Analysis of MOOC Discussion Forum
| 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.
| Basic programming knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
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.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
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.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes of discussion.

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.

Hints for preparing the presentation:
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.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
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.

=== 6. Course schedule===
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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2023)

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=TBD
|time=Friday 16:00 - 18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

Data Science for Smart City, we focus on one specific data, i.e., visual data (images and videos). We try to build a system that uses the data analysis methods to extract useful information. This part collaborated with the Goettingen government and the Goettingen bus company.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" 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.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Advanced Practical Course Data Science (Winter 2021/2022)

2021-10-26T14:03:19Z

Wwang: /* Course Organization */

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=TBD
|time=Friday 16:00 - 18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

In Smart City, we focus on one specific data, i.e., visual data (images and videos). We try to build a system that uses the data analysis methods to extract useful information. This part collaborated with the Goettingen government and the Goettingen bus company.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" 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.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Advanced Practical Course Data Science (Winter 2021/2022)

2021-10-26T13:58:20Z

Wwang: /* General Description */

Advanced Practical Course Data Science (Winter 2021/2022)

2021-10-26T13:57:40Z

Wwang: /* Prerequisites */

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=TBD
|time=Friday 16:00 - 18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" 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.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Advanced Practical Course Data Science (Winter 2021/2022)

2021-10-26T13:55:29Z

Wwang: /* Details */

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=TBD
|time=Friday 16:00 - 18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Seminar on Internet Technologies(Winter 2021/2022)

2021-09-13T09:10:32Z

Wwang: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|place=Through Zoom, waiting link
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=279555&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.

==Course description==

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.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

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.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''1th Nov. 2021 ''': Deadline for registration the course
* '''10th Feb. 2022 ''': Deadline for registration to attend the final presentation
* '''17th Feb. 2022 ''' : Final Presentations online (Throught Meetings in StudIP)
* '''22th March 2022 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|Event detection from microblog
| In this topic, you will study how to detect events from microblog, like Twiter.
| Basic programming knowledge, Basic machine learning knowledge
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|[https://dl.acm.org/doi/10.1145/3377939],[https://dl.acm.org/doi/10.1145/3184558.3186338],[https://ieeexplore.ieee.org/document/9094110],[https://dl.acm.org/doi/10.1145/3161193],[https://link.springer.com/chapter/10.1007%2F978-981-13-2922-7_7]
| Yes
|-
|-
|Graph neural network
| In this topic, you will study GNNs, like TCN and subgraph GNNs.
| Basic programming knowledge, Basic machine learning knowledge
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|[https://arxiv.org/pdf/2006.10637.pdf][https://arxiv.org/pdf/2006.10538.pdf] [https://arxiv.org/pdf/2006.07988.pdf]
| Yes
|-
|-
|Running neural-network-based applications on mobile devices
| In this topic, you will study how to partition application processing pipelines, e.g., scaling face recognition.
| Basic programming knowledge, Basic machine learning knowledge
| [Weijun Wang, weijun.wang@informatik.uni-goettingen.de]
|[https://dl.acm.org/doi/pdf/10.1145/3372224.3380881]
| Yes
|-
|-
|Running neural network on Multiple CPUs/GPUs or heterogeneous hardware
| In this topic, you will study how to fine-grained partition NN and schedule them on multiple hardware.
| Basic programming knowledge, Basic machine learning knowledge
| [Weijun Wang, weijun.wang@informatik.uni-goettingen.de]
|[https://dl.acm.org/doi/pdf/10.1145/3341301.3359658][https://dl.acm.org/doi/pdf/10.1145/3458864.3467882]
| Yes
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
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.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
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.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

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 for your presentation.

Hints for preparing the presentation:
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.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding bibliography, etc.).
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.

=== 6. Course schedule===
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.

[[Category:Courses]]

Advanced Practical Course Data Science (Winter 2021/2022)

2021-08-03T11:56:35Z

Wwang: /* Details */

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=TBD
|time=Thursday 16:00-18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Smart city (Summer 2021)

2021-08-02T09:15:49Z

Wwang: /* Schedule */

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 5-6 ECTS
|module=M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang, MSc. Weijun Wang];[http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|time=Wed. 14:00-16:00
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=282662&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20211&idcol=k_semester.semid&idval=20211&getglobal=semester]
}}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I: Course Setup [https://drive.google.com/file/d/1krd4swV3brbSAZwW4VzqVisbtu0IOp5x/view?usp=sharing] & Smart City (Online)
| No
|-
| align="right"|
w2
| Lecture II: Object Detection [https://drive.google.com/file/d/1Zw6JWEL25Czev4tyPoIuNcgNo4SAFNl7/view?usp=sharing] & System Architecture-Video Analytics [https://drive.google.com/file/d/1YdXExCJnOSpZLRY4UH1ltKWAFHW4sItJ/view?usp=sharing] (Online)
|
|-
| align="right"|
w3-4
| Warm-up: get familiar with your devices (OS boot[https://drive.google.com/file/d/1WZENpDHlkcxr2N3W1_Q03df1T3byVeu0/view?usp=sharing], last semester's final task description[https://drive.google.com/file/d/1Yt1MfIqo3zMy3VKgpFZ7paxLXHJ7Lb6g/view?usp=sharing] and students' report[https://pad.gwdg.de/s/I2xBpBN7R#Source-Code] and code[https://user.informatik.uni-goettingen.de/~ole.umlauft/content/SmartCity/])
| No
|-
| align="right"|
w5-8
|
Task 1: In this task, you will read, code, and write. Task description[https://drive.google.com/file/d/1qgubmUGBLd6xDlox_Y60VDLikbTdtFTH/view?usp=sharing]. There is no report format, you can write anything related to Task 1 you want but no less than one page.
|Report (due on 6th June)
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final report
|
|-
| align="right" |
24.08
| Final presentations
|
|-
|}

Advanced Practical Course Data Science (Winter 2021/2022)

2021-07-28T08:31:34Z

Wwang:

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]
|time=Thursday 16:00-18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Advanced Practical Course Data Science (Winter 2021/2022)

2021-07-28T08:31:00Z

Wwang:

{{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/fabian-wölk MSc. Fabian Wölk]
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]
|time=Thursday 16:00-18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
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.

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:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
| Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 5-6 ECTS
|module=M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang, MSc. Weijun Wang];[http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|time=Thurs. 14:00-16:00
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=282662&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20211&idcol=k_semester.semid&idval=20211&getglobal=semester]
}}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Teaching

2021-07-28T08:30:26Z

Wwang: /* Winter Semester 2021/2022 */

== Winter Semester 2021/2022 ==
* [[Computer Networks (Winter 2021/2022)|Computer Networks]] (BSc) (Fabian)

* [[Seminar on Internet Technologies(Winter 2020/2021) |Seminar on Internet Technologies(Winter 2020/2021)]] (MSc, BSc) (Tingting)

* [[Advanced Practical Course Data Science (Winter 2021/2022)|Advanced Practical Course Data Science (Winter 2021/2022) ]](MSc) (Weijun)

* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2021/2022)|Advanced topics in mobile and social computing (AToMSC) (Winter 2021/2022)]] (Tingting)

* [[Practical Course Networking Lab (Winter 2021/2022) | Practical Course Networking Lab ]] (BSc) (Yunxiao)

== Summer Semester 2021 ==
* [[Advanced Computer Networks (Summer 2021) | Advanced Computer Networks]] (MSc) (Bangbang)

* [[Computer Networks (Summer 2021) | Computer Networks (Exam only!) (Summer 2021)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2021) | Seminar on Internet Technologies]](MSc, BSc) (Jiaquan)
* [[Advanced Practical Course Data Science (Summer 2021) ]](MSc) (Fabian, Jiaquan)
* [[Advanced topics in mobile and social computing (AToMSC) (Summer 2021) | Advanced topics in mobile and social computing (AToMSC) (Summer 2021)]] (MSc, BSc) (Tingting)
* [[Practical Course Networking Lab (Summer 2021) | Practical Course Networking Lab ]] (BSc) (Cong, Bangbang)
* [[Smart city (Summer 2021)]] (MSc, BSc) (Weijun, Fabian)

== Winter Semester 2020/2021 ==
* [[Computer Networks (Winter 2020/2021) | Computer Networks]] (BSc) (Fabian,Yachao)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2020_2021) Seminar on Internet Technologies(Winter 2020/2021)] (MSc, BSc) (Tingting, Shichang, Sripriya)
* [[Advanced Practical Course Data Science (Winter 2020/2021) ]](MSc) (Jiaquan)
* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2020/2021) | Advanced topics in mobile and social computing (AToMSC) (Winter 2020/2021)]] (MSc, BSc) (Sripriya)
* [[Practical Course Networking Lab (Winter 2020/2021) | Practical Course Networking Lab ]] (BSc) (Cong, Bangbang)
* [[Smart city]] (MSc, BSc) (Fabian, Weijun,Tingting)

== Summer Semester 2020 ==
* [[Advanced Computer Networks (Summer 2020) | Advanced Computer Networks]] (MSc)
* [[Advanced topics in mobile and social computing (AToMSC) (Summer 2020) | Advanced topics in mobile and social computing (AToMSC) (Summer 2020)]] (MSc, BSc)
* [[Advanced Practical Course Data Science (Summer 2020) ]](MSc)
* [[Seminar on Internet Technologies (Summer 2020) ]](MSc, BSc)
* [[Computer Networks (Summer 2020) | Computer Networks (Exam only!) (Summer 2020)]] (BSc)
* [[Practical Course Networking Lab (Summer 2020) | Practical Course Networking Lab ]] (BSc)

== Winter Semester 2019/2020 ==
* [[Computer Networks (Winter 2019/2020) | Computer Networks]] (BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2019_2020) Seminar on Internet Technologies(Winter 2019/2020)] (MSc, BSc)
* [[Advanced Practical Course Data Science (Winter 2019/2020) ]](MSc)
* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2019/2020) | Advanced topics in mobile and social computing (AToMSC) (Winter 2019/2020)]] (MSc, BSc)
* [[Practical Course Networking Lab (Winter 2019/2020) | Practical Course Networking Lab ]] (BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ Advanced Blockchain] (MSc, BSc)

== Summer Semester 2019 ==
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Topics_in_Computer_Networks_(ATCN)_2019 Seminar ATCN] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Summer_2019) Seminar on Internet Technologies(Summer 2019)] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Computer_Networks_(Summer_2019)#Schedule_.28Tentative.29 Advanced Computer Networks (Summer 2019)] (MSc)
* [[Computer Networks (Summer 2019) | Computer Networks (Exam only!)]] (BSc)
* [[Practical Course Networking Lab (Summer 2019) | Practical Course Networking Lab ]] (BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ss/ Introduction to Blockchain Technology] (MSc, BSc)
* [[Advanced Practical Course Data Science (Summer 2019) ]](MSc)

== winter Semester 2018/2019 ==
* [https://www.stud.informatik.uni-goettingen.de/bcs/ws2018/ Introduction to Blockchain Technology (Examination Only)] (MSc, BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ws2018-advanced/ Advanced Blockchain] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Topics_in_Mobile_Communications_(AToMIC)_2018 Seminar ATCN/AToMIC] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2018) Seminar on Internet Technologies(Winter 2018/2019)] (MSc, BSc)
* [[Computer Networks (Winter 2018/2019) | Computer Networks]] (BSc)

== Summer Semester 2018 ==
* [https://www.stud.informatik.uni-goettingen.de/bcs/ss/ Introduction to Blockchain Technology] (MSc, BSc)
* [[Practical Course Data Science (Summer 2018) ]] (MSc)
* [[Practical Course Networking Lab (Summer 2018) | Practical Course Networking Lab ]] (BSc)
* [[Advanced Computer Networks (Summer 2018) | Advanced Computer Networks ]] (MSc)
* [[Computer Networks (Summer 2018) | Computer Networks (Exam only!)]] (BSc)
* [[Seminar on Internet Technologies (Summer 2018) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Computer Networks (Winter 2017/2018) | Computer Networks]] (BSc)
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)
* [[Seminar on Internet Technologies (Winter 2017/2018) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Advanced Practical Course Data Science (Winter 2021/2022)

2021-07-28T08:30:00Z

Wwang: Created page with "{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180..."

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 5-6 ECTS
|module=M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang, MSc. Weijun Wang];[http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|time=Thurs. 14:00-16:00
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=282662&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20211&idcol=k_semester.semid&idval=20211&getglobal=semester]
}}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Smart city (Winter 2021/2022)

2021-07-27T13:54:33Z

Wwang: /* Schedule */

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 5-6 ECTS
|module=M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang, MSc. Weijun Wang];[http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|time=Thurs. 14:00-16:00
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=282662&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20211&idcol=k_semester.semid&idval=20211&getglobal=semester]
}}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Smart city (Winter 2021/2022)

2021-07-27T13:53:14Z

Wwang: /* Details */

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 5-6 ECTS
|module=M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang, MSc. Weijun Wang];[http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|time=Thurs. 14:00-16:00
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=282662&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20211&idcol=k_semester.semid&idval=20211&getglobal=semester]
}}

==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''

'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''

==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.

This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.

* Help students to use computer science knowledge to build a practical AI system.

* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.

* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.

* Learn how to analyze city public transport sensor data.

For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:

* Depth camera (e.g. Intel RealSense D435)

* On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)

* Power supply (e.g. EC Technology Powerbank)

All these sub-systems in each bus will be combined into one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.

Tasks of students and implementation plan
The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.

The main tasks are as follows:

1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically.

2. Label corresponding objects/events in videos as the dataset.

3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video.
(We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together.)

4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results, and present the results in the final Smart City report.

a) The idea can be a new application.

b) The idea can also be an algorithm or module on how to improve the performance of the architecture.

Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of.

* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected.

* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''.

* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer.

* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Grading==
* Participation: 50%
** Task 1: 20%
** Task 2: 30%

* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
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.
All quoted images, tables and text need to indicate their source.
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.

* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
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.
The source code, data (or URL of data) and a manual should be uploaded with the report.

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I: Course Setup [https://drive.google.com/file/d/1krd4swV3brbSAZwW4VzqVisbtu0IOp5x/view?usp=sharing] & Smart City (Online)
| No
|-
| align="right"|
w2
| Lecture II: Object Detection [https://drive.google.com/file/d/1Zw6JWEL25Czev4tyPoIuNcgNo4SAFNl7/view?usp=sharing] & System Architecture-Video Analytics [https://drive.google.com/file/d/1YdXExCJnOSpZLRY4UH1ltKWAFHW4sItJ/view?usp=sharing] (Online)
|
|-
| align="right"|
w3-4
| Warm-up: get familiar with your devices (OS boot[https://drive.google.com/file/d/1WZENpDHlkcxr2N3W1_Q03df1T3byVeu0/view?usp=sharing], last semester's final task description[https://drive.google.com/file/d/1Yt1MfIqo3zMy3VKgpFZ7paxLXHJ7Lb6g/view?usp=sharing] and students' report[https://pad.gwdg.de/s/I2xBpBN7R#Source-Code] and code[https://user.informatik.uni-goettingen.de/~ole.umlauft/content/SmartCity/])
| No
|-
| align="right"|
w5-8
|
Task 1: In this task, you will read, code, and write. Task description[https://drive.google.com/file/d/1qgubmUGBLd6xDlox_Y60VDLikbTdtFTH/view?usp=sharing]. There is no report format, you can write anything related to Task 1 you want but no less than one page.
|Report (due on 6th June)
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
| Final presentations
|
|-
| align="right" |
24.08
| Final report
|
|-
|}

Wwang: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] and [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao, Sripriya Srikant Adhatarao]
|time=April 12th. '''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|place=Through Zoom, waiting link
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=266853&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Announcement==
Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.

==Course description==

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.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

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.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''17th Apr. 2021 ''': Deadline for registration the course
* '''16th Jun. 2021 ''': Deadline for registration to attend the final presentation
* '''24th Jun. 2021 (14:00-18:00)''' : Final Presentations online (waiting for the link)
* '''24th Aug. 2021 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|Event detection from microblog
| In this topic, you will study how to detect events from microblog, like Twiter.
| Basic programming knowledge, Basic machine learning knowledge
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|[https://dl.acm.org/doi/10.1145/3377939],[https://dl.acm.org/doi/10.1145/3184558.3186338],[https://ieeexplore.ieee.org/document/9094110],[https://dl.acm.org/doi/10.1145/3161193],[https://link.springer.com/chapter/10.1007%2F978-981-13-2922-7_7]
| Yes
|-
|-
|QoE modelling
| In this topic, you will explore how Quality of Experience (QoE) is modelled for multimedia services with machine learning.
| Basic programming knowledge, Basic machine learning knowledge
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|[https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8950450][https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8922616]
| Yes
|-
| The maximum throughput problem in quantum entangle routing
| In this topic, you will study the entanglement routing problem in a quantum network, which is a novel network built on quantum mechanics.
| Basic programming knowledge, Basic mathematical programming knowledge
| [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de]
|[https://dl.acm.org/doi/10.1145/3387514.3405853]
| Yes
|-
| Sensing for flood detection
| You will study methods to monitor water bodies (e.g. rivers) with sensors and how their data can be analyzed to detect upcoming floods and identify affected areas.
| Basic machine learning knowledge
| Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
| Modelling and simulations of floods
| You will study methods to model and simulate flood flows and how this can be used to identify affected areas of potential floods.
| Basic machine learning knowledge, Basic mathematical knowledge (knowledge in numerical mathematics can be advantageous)
| Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
| Super-resolution technique for efficient video delivery
| Super-resolution (SR) is one of the fundamental tasks in Computer vision. You will learn how to train and use it.
|Data Science and Computer Vision background, as well as programming skills like Python.
|Weijun Wang [weijun.wang@informatik.uni-goettingen.de]
|
| Yes
|-
|}

==Workﬂow==

=== 1. Select a topic ===
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.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
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.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

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 for your presentation.

Hints for preparing the presentation:
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.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).
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.

=== 6. Course schedule===
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.

[[Category:Courses]]

Theses and Projects

2021-02-13T14:58:46Z

Wwang: /* Open Theses and Student Project Topics */

== An introduction to the Computer Networks group ==

See a [https://wiki.net.informatik.uni-goettingen.de/w/images/5/5a/NETGroup_Poster-Jan2021.pdf poster] for a general overview, an [http://www.net.informatik.uni-goettingen.de/?q=research anchor] to our research activities, a list of [https://wiki.net.informatik.uni-goettingen.de/w/images/a/a3/Social_Computing_publications.pdf social computing related] or networking-related publications, and the
[http://www.net.informatik.uni-goettingen.de/?q=news/annual-report-2020-best-wishes-2021 annual report(s)] for our recent activities.

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project

=== Super resolution technique for efficient video delivery ===

* '''New!''' Super-resolution (SR) is one of the fundamental tasks in Computer vision. Video delivery on Internet or in WAN is important for various applications, eg., video analytics and video viewing. This project attempts to explore the potential of SR for video delivery. We expect you have Data Science and Computer Vision background, as well as programming skills like Python.

Please contact Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/P)

=== Road anomaly and driver behavior detection ===

New! Road situations such as road traffic, roadworks and damages are critical for both human and autonomous driving. For driving (or assisted) with humans, its important to detect how the driver behaves facing dynamic road situations. This project attempts to detect anomalous road situations and driver behaviors with multi-source data mining, fusion and machine learning techniques. We expect you have some data analytics and machine learning background, as well as programming skills like Python.
Please contact Prof. Xiaoming Fu (B/M/P).

=== Assessing city livability with big data ===

* '''New!''' City livability is related to a number of factors, such as quality of life, job satisfaction, environment (green space, CO2/PM2.5, schooling/health support etc), policy, commuting time, entertainment. We utilize different data sources to understand their relation to the city livability, and analyze the coherent features which offer an evaluation framework for a city's attractiveness and livability for different types of citizens. We expect you have some statistics and machine learning background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P).

=== Socioeconomic analysis on commuters ===

* '''New!''' Understanding the commuter behaviour and the factors that lead to commuting are more important today than ever before. With steadily increasing commuter numbers, the commuter traffic can be a major bottleneck for many cities. The increasing awareness of a good work-life balance leads to more people wanting shorter commuting distances. The commuter behaviour consequently plays an increasingly important role in city and transport planning and policy making. This topic aims to infer knowledge from commuter data, analyzing the influence of GDP, housing prices, family situation, income and job market on the decision to commute. We expect you have some statistics and machine learning background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P)

=== Socioeconomic Status and Internet Language Usage ===

* '''New!''' Numerous people write social media posts and exchange messages with colleagues, friends, acquaintances or even strangers on different platforms. We would like to understand how the underlying social class membership (socioeconomic status) affects Internet users' language use, by investigating the sociolinguistic features in users' posts/messages across a multitude of datasets and their relationship to their socioeconomic status. We expect you have some statistics and textual analysis/natural language processing background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P)

=== Multimedia Resource Allocation for QoE Improvement by Deep Learning===

* '''New!''' Deep learning has been widely used in various real-time applications and systems. Dynamic resource allocation for multimedia (e.g. Video) to improve QoE is an interesting topic. We need three students for this topic. We expect you have a background in deep learning and computer network, as well as programming skills like Python and Go.

(1) one to realize and improve the system for video transmission and network configuration according to resource allocation policy;
* You will use QUIC [https://github.com/lucas-clemente/quic-go] protocol (Go language) to implement network allocation and place the server part on AWS/other clouds.
(2) one to implement the deep learning algorithm to design the controller for dynamic resource allocations.

(3) one student for the QoE model using deep learning.

Please contact Dr.Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ] and Weijun Wang [weijun.wang@informatik.uni-goettingen.de](B/M/P)

=== Low Power, Wide Area (LPWA) technologies on smart cities===

* '''New!'''The LoRaWAN specification is a Low Power, Wide Area (LPWA) networking protocol, which is attracting a lot of attention due to their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In this project, we plan to exploit the LoRaWAN technologies to improve the performance of applications in smart cities. More details can be found in this [https://ieeexplore.ieee.org/abstract/document/7815384?casa_token=c3-nAktQO-AAAAAA:EHmi8hFe-HL853Kwq8Kot-mi8KPNSahLRT-4Tp0O8pdaT0mVH_DKUYPGU9onF227eKhpPPyC1436kw link] Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] (B/M/P)

=== Machine Learning & deep learning on electronic healthcare records===

In recent years, large amounts of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the quality of care delivery. This project will be mainly on using machine learning to analyze electronic healthcare dataset. Please contact [http://www.net.informatik.uni-goettingen.de/?q=people/yachao-shao Yachao Shao] (B/M/P)

=== Machine Learning or Deep learning Method (Graph-based) on Recommending system or Network Traffic ===

This project will be provide students an opportunity to learn how to use machine learning or deep learning methods (espeically graph-based DL method) to solve problems in recommending systems or computer networks. The requirements include: 1) like (python) coding; 2) willing to learn DL knowledge; 3) willing to read and learn open source projects;4) Regular meeting and discussion via skype and email. Please contact [sding@cs.uni-goettingen.de Shichang Ding](B/M/P)

=== Machine Learning for Security and Privacy in Networks ===
1) QUIC protocol design for video streaming analysis. (B/M/P, at least familiar with one programming language). Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] (Assigned to Yuhan Wang and Pronaya Prosun Das)

2) Implement algorithms for improving the network anomaly detection. (B/M/P, at least familiar with one programming language). Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] ====

3) Implement algorithms for improving the privacy of vehicle communications. (B/M/P, at least familiar with one programming language). Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan]

4) '''New!''' Privacy preservation for reinforcement learning. (B/M/P), at least familiar with one programming language-python. Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ].



== Ongoing Topics ==

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
|Bio-Data analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-mayutan-arumaithurai Mayutan Arumaithurai]
|
|
| Assigned to Lindrit
|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
| Build a personalized context-aware recommender system for customers according to their own interest.
|
|
|
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
| Design and Implementation of a distributed OSN on Home Gateways (Student project and Master's Thesis)
|[http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
|
| Completed by Dieter Lechler
|-
|}



===QUIC or Multipath QUIC Design===
* '''New!''' Implement algorithms for improving QUIC or Multipath QUIC performance. (B/M/P, at least familiar with one programming language (eg. [https://github.com/devsisters/libquic C++], [https://github.com/lucas-clemente/quic-go go] or Python).) Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] (Finished)

===Segment Routing based SDN===
* '''NEW! Winter 2018/2019 ''' There are many topics opened for Master and Bachelor theses and projects. Please contact [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]

=== Software Defined Networks (SDN) ===
* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]





=== Data Analysis with Bio data ===

* '''NEW! 2019 ' if you are interested in topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/?q=people/dr-mayutan-arumaithurai Mayutan Arumaithurai]

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking(finished) ===
* '''New!''' [[Goettingen Assistant: Android App Development (completed)]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
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