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Data Science in Smart City (Winter 2023/2024)

Revision as of 14:55, 26 September 2023 by Li56 (talk | contribs)
Imbox content.png Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.


Details

Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer: Prof. Xiaoming Fu; Zhengze Li
Teaching assistant: Zhengze Li, Yanlong Huang
Time: Monday 10:00 - 12:00am
Place: Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)
UniVZ [1]


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 data science pipeline
  • Exploratory data analysis
  • The Python Data Science stack
  • Video Analytics
  • Advanced algorithms for Data Science
  • Parameter tuning for predictive models

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.
  • Learn how to analyze city public transport sensor data.

Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester. A final report needs to be submitted at the end of the semester.

Prerequisites

  • You are highly recommended to have completed a course on Data Science (e.g., "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, Matlab or any language that features proper machine learning libraries

Schedule

When? What?
30.10.2023 Lecture 1
06.11.2023 Lecture 2
13.11.2023 Lecture 3 & Release of Task 1
20.11.2023 Intermediate meeting of Task 1
04.12.2023 Task 1 report submission (Before 10PM)
11.12.2023 No Lecture (Whit Monday)
18.12.2023 Lecture 4 & Release of Task 2
08.01.2024 Intermediate meeting of Task 2
15.01.2024 Task 2 report submission (Before 10PM)
22.01.2024 Release of Task 3
29.01.2024 Intermediate meeting Task 3
08.03.2024 Report Submitting
11.03.2024 Final Presentation

Where?: Room 0.101, Goldschmidtstr. 7 (Informatik u.Stochastik)

Grading

    • Task 1: 25%
    • Task 2: 25%
    • Task 3: 50% (Presentation: 20%, Report&Code: 30%)
  • Presentation:
    • Present on your work with a slide to the audience (in English).
    • 20 minutes of presentation followed by 10 minutes Q&A.

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:

The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[2]). 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.

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