242590 Mathematics of Machine Learning (S) (WiSe 2022/2023)

Short comment

This lecture will be given on zoom. The zoom link will be distributed prior to the start of the lecture via the participants email list.

Contents, comment

In this online course we aim to get acquainted with some of the recent progress in the mathematical understanding and theory of machine learning. Particular emphasis will be laid upon the implicit bias in supervised learning, as well as upon introductory aspects of unsupervised learning and reinforcement learning.

The aim of the course is to get an overview of a series of recent articles and the methods developed therein.

The course is directed to non experts with a solid background in mathematics aiming to get an idea of recent progress in the mathematics of machine learning.

Link to seminar webpage (password on request via Anja Baum): http://www.bgess.de/index.php/teaching/bielefeld-leipzig-online-seminar-ws21-22-mathematics-of-machine-learning/

External comments page

http://www.bgess.de/index.php/teaching/bielefeld-leipzig-online-seminar-ws21-22-mathematics-of-machine-learning/

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
wöchentlich Mi 16:15-17:45 ONLINE   10.10.2022-03.02.2023

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Subject assignments

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Mathematik / Promotion Subject-specific qualification   2 aktive Teilnahme  

Active contribution in form of a presentation of a research paper (to be chosen) and a handout summarizing the presentation is required for each participant. You should be prepared to spend a significant amount of time on this course.

No eLearning offering available
Address:
WS2022_242590@ekvv.uni-bielefeld.de
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Notes:
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Last update basic details/teaching staff:
Friday, September 23, 2022 
Last update times:
Thursday, August 4, 2022 
Last update rooms:
Thursday, August 4, 2022 
Type(s) / SWS (hours per week per semester)
Seminar (S) / 2
Department
Fakultät für Mathematik
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369431168