Theoretical and Methodological Foundations of Autonomous Systems/Maths for Intelligent Systems (WS 20/21)

Lecturer: Jim Mainprice

TA: Philipp Kratzer, Yoojin Oh, Janik Hager


  • Lectures on Tuesdays 14:00 – 15:30 from 03.11.2020 to 09.02.2021
  • Tutorials


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 geometric intuition behind more abstract ideas with concrete examples taken from robotics and machine learning.

Tentative Outline

  1. Introduction
  2. Linear Algebra
  3. Analytic Geometry
  4. Matrix Decomposition
  5. Vector Calculus
  6. Differential Geometry
  7. Differential Equations
  8. Probabilities
  9. Optimization I
  10. Optimization II
  11. Math Problems in Intelligent Systems I
  12. Math Problems in intelligent Systems II


Course Material will be uploaded to ilias:

Mathematics for machine learning book:

Previous years material: