Mathematical Foundations for Machine Learning
30.05. - 01.07.
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 30.05. and 24.06. is given below:
Tue 10:15 - 11:45 am
Thu 10:15 - 11:45 am
Exercise sheets will be collected in the lecture.
The date for the online test in the week between 27.06 and 01.07. will be announced.
- 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 - 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 until 15.05. but not necessary to attend the course, students of all fields and universities are invited. The decision wether the course will take place in person or online, depends on the number of attendees, and will be announced about around the 23.05.