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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Machine Learning for Computer Vision (IN2357) (2h + 2h, 5ECTS)

SS 2018, TU München


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

April, 17th: The rooms for both lecture and tutorial have changed. See below.

April, 20th: New frequently asked questions section. See below.

June, 25th: There will be no tutorial on Thursday, June 28.

July 15th: There will be no repeat exam in SS2018.

July 25th: No cheatsheets, calculators or other assistances are allowed in the exam.


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: MW 0350 (Egbert von Hoyer)
Date: Fridays, starting from April 13th
Time: 14.00 - 16.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2


Location: Interimshörsaal 2
Date: Thursdays, starting from April 19th
Time: 16.00 - 18.00
Lecturer: John Chiotellis, Maximilian Denninger
SWS: 2

Office Hours

Location: 02.09.058
Date: Wednesdays
Time: 14.00 - 15.00


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

Tentative Schedule
Topic Lecture Date Tutorial Date
Introduction / Probabilistic Reasoning 13.04 19.04
Regression 20.04 26.04
Graphical Models I 27.04 03.05
Graphical Models II 04.05 10.05
Bagging and Boosting 11.05 17.05
Metric Learning 18.05 24.05
Deep Learning 25.05 ?
Sequential Data / Hidden Markov Models 1.06 07.06
Kernels and Gaussian Processes 08.06 14.06
Clustering 1 15.06 21.06
Clustering 2 22.06 -
Variational Inference I 29.06 05.07
Variational Inference II 06.07 12.07
Sampling Methods 13.07 ?


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 .

Lecture Slides

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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