Masterseminar Probability Theory (Alsmeyer, Dereich, Kabluchko)

WS 2022/23

Tuesdays 16:15, SRZ 203

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Dozent: Prof. Dr. Gerold Alsmeyer, Prof. Dr. Steffen DereichProf. Dr. Zakhar Kabluchko

This event in the course overview


In this seminar, we discuss advanced topics related to probability theory. Topics come from one of the following areas:

Stochastic processes

Machine learning

Stochastic geometry


Possible topics in Machine Learning (referring to the book "Mathematics of Data Science", see material)


M1: Matrix concentration inequalities (Sect. 6.4, p. 91-97)


M2: The Johnson-Lindenstrauss Lemma (Section 9.1, p. 123-130)


M3: Gordon's theorem (Sect. 9.2, p. 130-134)

(one might fill up the presentation with additional topics from Sect. 9.3 or include the proof of Slepian's lemma or Thm 9.15)

M4: Compressive Sensing (Sect. 10.1, p. 142-148; include motivation of introduction to Sect. 10)

Further topics will be posted soon.