240197 Topics of Statistical Learning (S) (SoSe 2026)

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Experimental machine learning uniquely advances ahead of theory.
Practitioners identify and exploit phenomena that are yet theoretically void. We discuss recent research at the intersection of statistics and machine learning. The aim is to stay current on both theoretical and experimental advances.

A nonexhaustive list of topics includes transfer learning, RKHS theory, algebraic statistics, high dimensional statistics, and manifold estimation.

Participants from all fields are welcome to attend and/or get involved.

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
wöchentlich Do 14-16   13.04.-24.07.2026

Subject assignments

Module Course Requirements  
24-M-P1 Profilierung 1 Profilierung 1 Profilierungsseminar Study requirement
Student information
24-M-P1a Profilierung 1 Teil A Profilierung 1 Teil A Profilierungsseminar Study requirement
Student information
24-M-P1b Profilierung 1 Teil B Profilierung 1 Teil B Profilierungsseminar Study requirement
Student information
24-M-P2 Profilierung 2 Profilierung 2 Profilierungsseminar Study requirement
Student information
24-M-PT-ST5a Ausgewählte Kapitel der Wahrscheinlichkeitstheorie und Statistik 1 Ausgewählte Kapitel der Wahrscheinlichkeitstheorie und Statistik 1 Seminar Selected Topics in Probability Theory and Statistics Study requirement
Graded examination
Student information
24-M-PT-ST5b Ausgewählte Kapitel der Wahrscheinlichkeitstheorie und Statistik 2 Ausgewählte Kapitel der Wahrscheinlichkeitstheorie und Statistik 2 Seminar Selected Topics in Probability Theory and Statistics Study requirement
Graded examination
Student information
24-M-PWM Profilierung Wirtschaftsmathematik Profilierung Wirtschaftsmathematik Profilierungsseminar Study requirement
Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.

No more requirements
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Address:
SS2026_240197@ekvv.uni-bielefeld.de
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Last update basic details/teaching staff:
Friday, January 9, 2026 
Last update times:
Friday, January 9, 2026 
Last update rooms:
Friday, January 9, 2026 
Type(s) / SWS (hours per week per semester)
Seminar (S) / 2
Language
This lecture is taught in english
Department
Fakultät für Mathematik
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663695020