### Lecturer: Jim Mainprice

### TA: Philipp Kratzer, Yoojin Oh, Janik Hager

# Dates

- Lectures on Tuesdays 9:45 – 11:15 (first lecture on
**19.10.21**) in room V38.01

- Tutorials on Thursdays 9:45 – 11:15 (first tutorial on
**04.11.21**) in room V38.03

# Description

This course prepares students for further study of autonomous robotics, machine learning and artificial intelligence. Methodologies in autonomous systems are highly mathematical and require a concrete understanding of core areas such as linear algebra, functional analysis, differential geometry, optimization, probability, statistics, and decision theory. The course especially emphasizes intuition behind more abstract ideas with concrete examples taken from robotics and machine learning.

# Tentative Outline

- 10/19 – Introduction
- 10/26 – Linear Algebra
- 11/02 – Analytic Geometry
- 11/09 – Matrix Decomposition
- 11/16 – Vector Calculus
- 11/23 – Differential Equations
- 11/30 – Differentiable Manifolds
- 12/07 – Rotations in 3D (Classical groups)
- 12/14 – Probabilities
- 12/21 – Optimization I
- 11/01 – Optimization II
- 18/01 – Math Problems in Intelligent Systems I
- 25/01 – Math Problems in intelligent Systems II

# Material

- Course Material will be uploaded to ilias
- Mathematics for machine learning book: https://mml-book.github.io/book/mml-book.pdf