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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Machine Learning for Robotics and Computer Vision (IN3200) (2h + 2h, 5ECTS)

WS 2017, TU München


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

Beginning from Monday, 13.11.2017, the tutorial will be taking place in Room 00.08.059.

There is no tutorial on Monday, 20.11.2017.


Some students noted that their exam registration status in TUMonline is: "registered (preliminary registration)" with an exclamation mark in yellow circle. As far as we know that does not affect you. You can come to the exam.

No cheatsheets, calculators or other assistances are allowed.

There is NO repeat exam. The course is offered again in the next semester.


Location: CH 27402, Walter-Hieber-Hörsaal (5407.01.740B)
Date: Fridays, starting from October 20th
Time: 10.15 - 12.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2


Location: 00.08.059 NEW!
Date: Mondays, starting from October 23rd
Time: 14.00 - 16.00
Lecturer: John Chiotellis, Maximilian Denninger
SWS: 2
Office hours: Wednesdays, 13.30 - 14.30


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.

Tentative Schedule
Topic Lecture Date Tutorial Date
Introduction / Probabilistic Reasoning 20.10 23.10 and 30.10
Regression 27.10 6.11
Graphical Models (directed) 3.11 13.11
Graphical Models (undirected) 10.11 20.11
Metric Learning 17.11 27.11
Bagging and Boosting 24.11 4.12
Sequential Data / Hidden Markov Models 1.12 11.12
Kernels and Gaussian Processes 8.12 18.12
Deep Learning 15.12 15.1
Clustering 1 12.1 22.1
Clustering 2 19.1 29.1
Variational Inference 1 26.1 5.2
Variational Inference 2 2.2 5.2
Sampling Methods 9.2 12.2


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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Rick Szeliski (University of Washington) will give a talk in the TUM AI lecture series on Jan 28th, 5pm! Livestream


Frank Dellaert (Georgia Tech) will give a talk in the TUM AI lecture series on Dec 17th, 4pm! Livestream


Jon Barron (Google) will give a talk in the TUM AI lecture series on Oct 22nd, 9pm! Livestream