MA-INF 4228: FOUNDATIONS OF DATA SCIENCE

Summer Semester 2025

Content:

What is the Foundation of Data Science course about?

Data science aims at making sense of big data. To that end, various tools have to be understood to help in analyzing the arising structures. Often data comes as a collection of vectors with a large number of components. Understanding their common structure is the first main objective of understanding the data. The geometry and the linear algebra behind them become relevant and enlightening. Yet, the intuition from low-dimensional space turns out to be often misleading. We need to be aware of the particular properties of high-dimensional spaces when working with such data.

Fruitful methods for the analysis include singular vector decomposition from linear algebra and supervised and unsupervised machine learning.

Logistics:

  • Lectures: will take place in Room 0.109 (B-IT-Max) (Friedrich-Hirzebruch-Allee 6). ZOOM LINK
  • Tuesday: 10:15 AM - 11:45 AM
  • Thursday: 10:15 AM - 11:45 AM
     
  • Exercises: will take place in Room 0.109 (B-IT-Max) (Friedrich-Hirzebruch-Allee 6). ZOOM LINK
  • Tuesday: 12:15 PM - 13:45 AM
     
  • Course Materials: will be uploaded every week on eCampus.
     
  • Contact: Students should ask all course-related questions in our forum discussion on eCampus. For external inquiries, emergencies, or personal matters, you can contact Prof. Flek or David.
     
  • Office Hours: Please reach out to us first via mail to arrange any in-person meeting.
    • Prof. Dr. Lucie Flek: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.123
    • David Kaczér: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.120
    • Vahid Sadiri Javadi: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.126

NEWS / UPDATES:

  • 25.03.2025: The first lecture will take place on Thursday, 08.04.2025 at 10:15 AM in Room 0.109 (B-IT-Max).
  • 25.03.2025: The first exercise will take place on Tuesday, 08.04.2025 at 12:15 PM in Room 0.109 (B-IT-Max).

Instructors:

Prof. Dr. Lucie Flek

flek(at)bit.uni-bonn.de

Head of CAISA Lab

David Kaczér

dkaczer(at)uni-bonn.de

Exercise Instructor

Vahid Sadiri Javadi

vahidsj(at)bit.uni-bonn.de

Course Coordinator

Coursework:

Assignments (Prerequisite for the exam):

  • Assignments:
    • Assignment 0 (10%): 
    • Assignment 1 (10%): 
    • Assignment 2 (10%): 
    • Assignment 3 (10%): 
    • Assignment 4 (10%): 
    • Assignment 5 (10%): 
    • Assignment 6 (10%): 
    • Assignment 7 (10%): 
    • Assignment 8 (10%): 
    • Assignment 9 (10%): 
  • Deadline: All assignments are due on Monday (one day before the exercise class) at 11:59 PM. All deadlines are listed in the schedule.
  • Submission: The assignment should be submitted via eCampus. Please do not email us your assignment.
  • Collaboration: Each student should work on the assignment individually. Please name your file properly. File name: <Assignment_<NUMBER>.pdf
  • **NOTE:** You need to achieve at least 50% of all the points to be allowed to take the exam.

Exam (100%):

  • Exam dates: will be announced as soon as we receive the rooms and dates from the examination office.
  • Allowed material: Pens and non-programmable calculators are permitted.

Allocation:

  • Master in Media Informatics: 8 ECTS credits
  • Master in computer science at University of Bonn: MA-INF 4228 9 CP
  • Students must register for the exam on POS/BASIS.

Literature:

  • Avrim Blum, John Hopcroft, and Ravindran Kannan (2020). Foundations of Data Science. Cambridge University Press, ISBN 9781108485067, eISBN 9781108620321.
    Drafts are on Hopcroft's page: PDF.
  • Olivier Bousquet, Stéphane Boucheron & Gábor Lugosi (2004). Introduction to Statistical Learning Theory. In Bousquet, v. Luxburg & G. Rätsch (editors), Advanced Lectures in Machine Learning, Springer, pp. 169--207, 2004. Webpage, PDF.
  • Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong (2019). Mathematics for Machine Learning. Webpage with PDF.

Schedule

WeekDateDescriptionEventsDeadlines
Week 1Lecture (Tue 08.04.2025)   
Exercise (Tue 08.04.2025) Assignment 0 OUT 
Lecture (Thu 10.04.2025)   
Week 2Lecture (Tue 15.04.2025)   
No Exercise   
Lecture (Thu 17.04.2025)   
Week 3Lecture (Tue 22.04.2025)   
Exercise (Tue 22.04.2025) Assignment 1 OUTAssignment 0 DUE
Lecture (Thu 24.04.2025)   
Week 4Lecture (Tue 29.04.2025)   
Exercise (Tue 29.04.2025) Assignment 2 OUTAssignment 1 DUE
Holiday (Labor Day)   
Week 5Lecture (Tue 06.05.2025)   
Exercise (Tue 06.05.2025) Assignment 3 OUTAssignment 2 DUE
Lecture (Thu 08.05.2025)   
Week 6Lecture (Tue 13.05.2025)   
Exercise (Tue 13.05.2025) Assignment 4 OUTAssignment 3 DUE
Lecture (Thu 15.05.2025)   
Week 7Lecture (Tue 20.05.2025)   
Exercise (Tue 20.05.2025) Assignment 5 OUTAssignment 4 DUE
Lecture (Thu 22.05.2025)   
Week 8Lecture (Tue 27.05.2025)   
Exercise (Tue 27.05.2025) Assignment 6 OUTAssignment 5 DUE
Holiday (Ascension Day)   
Week 9Lecture (Tue 03.06.2025)   
Exercise (Tue 03.06.2025) Assignment 7 OUTAssignment 6 DUE
Lecture (Thu 05.06.2025)   
Week 10No Lecture   
No Exercise   
No Lecture   
Week 11Lecture (Tue 17.06.2025)   
Exercise (Tue 17.06.2025) Assignment 8 OUTAssignment 7 DUE
Holiday (Corpus Christi)   
Week 12Lecture (Tue 24.06.2025)   
Exercise (Tue 24.06.2025) Assignment 9 OUTAssignment 8 DUE
Lecture (Thu 26.06.2025)   
Week 13Lecture (Tue 01.07.2025)   
Exercise (Tue 01.07.2025)  Assignment 9 DUE
No Lecture