This semester, the lecture will be given in presence. The lecture room is "102, Hörsaal 2, "Interims I" (5620.01.102)".

You can use our library for the programming exercises: mlcv-tutorial

Link for piazza: https://piazza.com/tum.de/spring2022/in2357

1. Attendance to the lecture is open for all students.

2. If your pursuing degree is not in Computer Science and you want to take the exam, you should ask the administrative staff responsible for your degree whether that is possible (it most probably is).

3. If you are a LMU student and you want to take the exam, you should ask the administrative staff responsible for your degree whether that is possible (it most probably is).

4. There is no possibility to get extra points for your final grade, such as bonus exercises, etc.

Location: Lecture hall Interims I (5620.01.102)
Date: Fridays, starting from May 6
Time: 12.00 - 14.00
Main Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Location: Lecture hall Interims I (5620.01.102)
Date: Thursday, starting from May 12
Time: 16.00 - 18.00
Lecturer: Dominik Schnaus
SWS: 2

In this lecture, the students will be introduced into the most frequently used machine learning methods in computer vision and robotics applications. The major aim of the lecture is to obtain a broad overview of existing methods, and to understand their motivations and main ideas in the context of computer vision and pattern recognition.

For material from previous semesters, please refer to, e.g.: WS2019

Linear Algebra, Calculus and Probability Theory are essential building blocks to this course. The homework exercises do not have to be handed in. Solutions for the programming exercises will be provided in Python .

Tentative Schedule