Foundations of data science

This course is listed

• in Aachen RWTHonline as Foundations of data science,
• in Bonn Basis as MA-INF 4228 Foundations of data science.

Direct SIGN UP to eCampus. (For visiting see below.)

• Online lecture room.
• eCampus pages of the course.
• Sciebo folder with notes and exercises.
• Free room.
• Mailing list: 21ss-fds-at-lists.bit.uni-bonn.de and its Info page.

Data science aims at making sense of big data. To that end various tools have to be understood for helping in analyzing the arising structures.

Often data comes as a collection of vectors with a large number of components. To understand 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. If time permits we also consider random graphs which are the second most used model for real world phenomena.

Michael Nüsken

• Monday, 12¹⁵ c.t.-14⁰⁰, online lecture room.
• Wednesday, 12¹⁵ c.t.-14⁰⁰, online lecture room.
• Tutorial 1: Monday, 14⁰⁰ c.t.-16⁰⁰, online tutorial room (via eCampus).
• Tutorial 2: Monday, 16⁰⁰ c.t.-18⁰⁰, online tutorial room (via eCampus). [Since 10 May 2021.]

First meeting: Monday, 12 April 2021, 12¹⁵-16⁰⁰ with a break: online room.

To ease your communication you can at any time appoint with each other in this free room.

You will find notes and exercises at sciebo until March 2022.

Lecture recordings, exercise handin and feedback are handled via the eCampus pages of the course.

Needed tools for the exam:

• Working camera and connection to BigBlueButton.
• Access to eCampus.
• Show (or print) a PDF.
• Digitize several pages into a PDF and upload PDF to eCampus. (Scanner or camera...)
• Paper, pens, ...

Allowed material:

• all teaching materials from the lecture and the tutorial,
• as well as all of your own materials and notes,
• a calculator, a python or sage session,
• cheat sheet (handwritten!, A4, double sided).

Detailed information is collected here.

Technical setup test: Monday, 19 July 2021, 1400-1500, tutorial 1 room.

Pre-exam meeting: Wednesday, 18 August 2021, 10⁰⁰-12⁰⁰, online lecture room.

Exam1: Friday, 20 August 2021, 10⁰⁰-13⁰⁰, online (eCampus, BBB).

Post-exam1: individual exam reviews.

Exam2: Tuesday, 28 September 2021, 10⁰⁰-13⁰⁰, online (eCampus, BBB).

Post-exam2: individual exam reviews.

• 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.

4+2 SWS.

• Master in Media Informatics:
  8 ECTS credits.
  Students have to register this course with RWTHonline.
• Master in Computer Science at University of Bonn: MA-INF 4228.
  9 CP.
  Students have to register for the exam to this course with POS/BASIS (see here).

Students are encouraged to ask and answer any questions related to the course on the mailing list:

21ss-fds-at-lists.bit.uni-bonn.de

You can subscribe to and unsubscribe from the mailing list using the information given on the list's Info page.