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

April, 24th: Link for piazza: https://piazza.com/tum.de/spring2019/in2357

1. Attendance to the lecture is open for all.

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 way to get extra points for your final grade, such as bonus exercises, etc.

Location: 5620.01.102 Interims Hörsaal 2
Date: Fridays, starting from April 26th
Time: 12.00 - 14.00
Important! New Starting time: 12:15!
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Location: 5620.01.102 Interims Hörsaal 2
Date: Thursdays, starting from May 2nd
Time: 16.00 - 18.00
Lecturer: John Chiotellis, Maximilian Denninger, Martin Sundermeyer
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.

Note that the lecture has a new module number now. In earlier semesters it was IN3200, now it is IN2357. The content is however (almost) the same. For material from previous semesters, please refer to, e.g.: WS2017

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 .