Theoretical and Methodological Foundations of Autonomous Systems/Maths for Intelligent Systems (WS 21/22)

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

  1. 10/19 – Introduction
  2. 10/26 – Linear Algebra
  3. 11/02 – Analytic Geometry
  4. 11/09 – Matrix Decomposition
  5. 11/16 – Vector Calculus
  6. 11/23 – Differential Equations
  7. 11/30 – Differentiable Manifolds
  8. 12/07 – Rotations in 3D (Classical groups)
  9. 12/14 – Probabilities
  10. 12/21 – Optimization I
  11. 11/01 – Optimization II
  12. 18/01 – Math Problems in Intelligent Systems I
  13. 25/01 – Math Problems in intelligent Systems II

Material