Lecturer: Jim Mainprice
TA: Philipp Kratzer, Yoojin Oh, Janik Hager
Announcements
- Please join the lecture using either of the methods:
- The lecture will be hosted in Jim’s Webex room at 14:00 (https://unistuttgart.webex.com/meet/jim.mainprice). Please join with you camera disabled!
- The lecture will also be live on our YouTube Channel (https://www.youtube.com/channel/UCVe0W0-tDpHaTuxLB8DmV2g)
Dates
- Lectures on Tuesdays 14:00 – 15:30 from 03.11.2020 to 09.02.2021
- Tutorials on Thursdays 14:00 – 15:30 from 12.11.2020 (a Webex invitation was sent by e-mail which and can also be found on the ilias forum)
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 geometric intuition behind more abstract ideas with concrete examples taken from robotics and machine learning.
Tentative Outline
- Introduction
- Linear Algebra
- Analytic Geometry
- Matrix Decomposition
- Vector Calculus
- Differential Geometry
- Differential Equations
- Probabilities
- Optimization I
- Optimization II
- Math Problems in Intelligent Systems I
- Math Problems in intelligent Systems II
Material
- Course Material will be uploaded to ilias: https://ilias3.uni-stuttgart.de/ilias.php?ref_id=2121859
- Mathematics for machine learning book: https://mml-book.github.io/book/mml-book.pdf
- Previous years material: https://www.user.tu-berlin.de/mtoussai//teaching/19-Maths/