Mathematical Foundations for Machine Learning


The goal of this course is to freshen and deepen the mathematical foundations from the computer science program that are necessary for the lectures Cognitive Algorithms and Machine Learning.

Topics of the course come from analysis (differentiation), linear algebra (vector spaces, dot products, orthogonal vectors, matrices as linear maps, determinants, eigenvalues and eigenvectors) and probability theory (multivariate probability distributions, calculations with expectation values and variances).


The weekly structure between the 5.6. and 7.7. is given below:

Exercise sheets will be collected in the lecture.

Preliminary structure:


The course is part of the module Machine Learning 1-X (M.Sc. Informatik) and optional for Cognitive Algorithms (B.Sc. Informatik).

Registration is desired but not necessary to attend the course. Students of all fields and universities are invited.

IDA Wiki: Main/SS23_MathML (last edited 2023-04-06 13:07:07 by ThomasSchnake)