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|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu] | |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu] | ||
|ta=MSc. Fabian Wölk (fabian.woelk@cs.uni-goettingen.de), MSc. Weijun Wang (weijun.wang@informatik.uni-goettingen.de), Dr. Tingting Yuan (tingt.yuan@hotmail.com) | |ta=MSc. Fabian Wölk (fabian.woelk@cs.uni-goettingen.de), MSc. Weijun Wang (weijun.wang@informatik.uni-goettingen.de), Dr. Tingting Yuan (tingt.yuan@hotmail.com) | ||
|time= | |time=Wed. 14:00-16:00 | ||
|place= | |place= mostly will be online | ||
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=270448&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | |univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=270448&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | ||
}} | }} | ||
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'''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. | '''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== | ==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. | 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 | 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: | The goal of this course is to: | ||
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*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. | *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 | *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: 10% | |||
** Task 2: 20% | |||
** Task 3: 20% | |||
* 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== | ==Schedule== | ||
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|{{Hl2|width =0.2}} |'''Time''' | |{{Hl2|width =0.2}} |'''Time''' | ||
|{{Hl2|width =0.5}} |'''Topic''' | |{{Hl2|width =0.5}} |'''Topic''' | ||
|{{Hl2}} |''' | |{{Hl2}} |'''Output''' | ||
|- | |- | ||
| align="right" | | | 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"| | | align="right"| | ||
w3 | |||
| | | Warm-up | ||
| | | No | ||
|- | |- | ||
| align="right" | | align="right"| | ||
| | w4-5 | ||
| | | | ||
Task 1 | |||
|Report | |||
|- | |- | ||
| align="right" | | align="right"| | ||
| | w6-8 | ||
|Task 2 | |||
| Task | |Report | ||
| | |||
|- | |- | ||
| align="right" | | align="right" | | ||
w9-14 | |||
| | |||
| Task 3 | | Task 3 | ||
| | | | ||
|- | |- | ||
| align="right" | | align="right" | | ||
15.03 | |||
| Final presentations | |||
| | |||
| | |||
| | | | ||
|- | |- | ||
| align="right" | | align="right" | | ||
31.03 | |||
| Final report | |||
| | |||
| | | | ||
|- | |- | ||
|} | |} | ||
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