## Mathematical Foundations for Machine Learning

**Date:**1.10.18 - 5.10.18

**Room:**MAR 0.011

**Exam:**12.10.18, 10 - 12h in MA 005

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

### 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).

More information can be found on ISIS.

### 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:

- 01.10. - Linear algebra part 1
- 02.10. - Linear algebra part 2
03.10. -

*No classes*(Tag der Deutschen Einheit)- 04.10. - Analysis
- 05.10. - Probability theory

### Credits

The course is compulsory for the modules Machine Learning 1 (M.Sc. Informatik) and Cognitive Algorithms (B.Sc. Informatik).

**Registration is not necessary**, students of all fields an universities are invited. Basis for the grading (2 SWS / 2 LP) is an exam (90 minutes). We can also provide a grading free *Schein* after a passed exam. Prerequisite for the participation in the exam 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.