MA-INF 4228: FOUNDATIONS OF DATA SCIENCE
Summer Semester 2024
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
- 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 Vahid.
- 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
- Vahid Sadiri Javadi: Friedrich-Hirzebruch-Allee 6 (B-IT) – Room: 2.120
NEWS / UPDATES:
- 25.02.2024: The first lecture will take place on Thursday, 18.04.2024 at 10:15 AM in Room 0.109 (B-IT-Max).
- 25.02.2024: The first exercise will take place on Tuesday, 23.04.2024 at 12:15 PM in Room 0.109 (B-IT-Max).
Instructors:
Coursework:
Assignments (Prerequisite for the exam):
- Assignments:
- Assignment 1 (8%):
- Assignment 2 (8%):
- Assignment 3 (8%):
- Assignment 4 (8%):
- Assignment 5 (8%):
- Assignment 6 (8%):
- Assignment 7 (8%):
- Assignment 8 (8%):
- Assignment 9 (8%):
- Assignment 10 (8%):
- 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: Each student should submit at least 5 assignments. 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
- Grading/ Feedback (2%): Each student should grade/ correct at least 5 assignments during the semester.
- **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 calculator 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
| Week | Date | Description | Events | Deadlines |
|---|---|---|---|---|
| Week 1 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 1 OUT | |||
| Lecture (Thu ) | ||||
| Week 2 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 2 OUT | Assignment 1 DUE | ||
| Lecture (Thu ) | ||||
| Week 3 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 3 OUT | Assignment 2 DUE | ||
| Lecture (Thu ) | ||||
| Week 4 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 4 OUT | Assignment 3 DUE | ||
| Lecture (Thu ) | ||||
| Week 5 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 5 OUT | Assignment 4 DUE | ||
| Lecture (Thu ) | ||||
| Week 6 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 6 OUT | Assignment 5 DUE | ||
| Lecture (Thu ) | ||||
| Week 7 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 7 OUT | Assignment 6 DUE | ||
| Lecture (Thu ) | ||||
| Week 8 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 8 OUT | Assignment 77 DUE | ||
| Lecture (Thu ) | ||||
| Week 9 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 9 OUT | Assignment 8 DUE | ||
| Lecture (Thu ) | ||||
| Week 10 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 10 OUT | Assignment 9 DUE | ||
| Lecture (Thu ) | ||||
| Week 11 | Lecture (Tue ) | |||
| Exercise (Tue ) | Assignment 10 DUE | |||
| Lecture (Thu ) | ||||
| Week 12 | Lecture (Tue ) | |||
| Exercise (Tue ) | ||||
| Lecture (Thu ) | ||||
| Week 13 | Lecture (Tue ) | |||
| Exercise (Tue ) | ||||
| Lecture (Thu ) |