Mathematical Foundations for Machine Learning
Oct 31st - Dec 9th
Thomas Schnake (firstname.lastname@example.org)
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 31.10. and 02.11. is given below:
Tue 10:15 - 11:45 am
Thu 10:15 - 11:45 am
Exercise sheets will be collected in the lecture.
- Week 1 - Linear Algebra I: Groups, Fields and Euclidean Vector Spaces
- Week 2 - Linear algebra II: Linear Transformations, Matrices and Determinants
- Week 3 - Analysis: Differentiation and ML Examples
- Week 4 - Probability Theory
- Week 5 - Selected Subject - Mathematics in Machine Learning Today
- Week 6 - Online Test
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. The course will take place online, yet the final test will be held in person.