## Mathematical Foundations for Machine Learning

**Date:**27.10.20 - 30.10.20

**Test:**13.11.20 (room and time TBA -- probably online)

**Lecturer:**Jacob Kauffmann (j.kauffmann@tu-berlin.de)

**ISIS Course:**

### Informations

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).

### Structure

Attendance is not mandatory. The structure is roughly given below:

10:00 – 11:30 am |
Introductory lecture |

11:30 – 3:00 pm |
Work on exercise sheets |

3:00 – 4:00 pm |
Review of exercise sheets |

4:00 – 5:00 pm |
Work on homework sheets |

Preliminary structure:

- 27.10. - Linear Algebra I: Groups, Fields and Euclidean Vector Spaces
- 28.10. - Linear algebra II: Linear Transformations, Matrices and Determinants
- 29.10. - Analysis: Differentiation and ML Examples
- 30.10. - Probability Theory

### Credits

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

**Registration is not necessary**, students of all fields an universities are invited. Basis for passing the course is a test (90 minutes). Prerequisite for the participation in the test is the achievement of at least half of all possible points in the homework, the results in the exercises are not included in the grade.