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TUM Department of Informatics
Technical University of Munich

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

SS 2022, TU München

Announcements

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

FAQ

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.

Lecture

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

Tutorial

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

Contents

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

Prerequisites

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
Topic Notes Lecture Date Tutorial Dates
Introduction / Probabilistic Reasoning Lecture in presence 6.05. 12.05.
Regression Lecture in presence 13.05. 19.05.
Logistic Regression Lecture in presence 20.05. 2.6.
Graphical Models Lecture in presence 27.05. 2.6.
Kernel Methods and Gaussian Processes I 3.6. 9.6.
Kernel Methods and Gaussian Processes II 10.06. 23.06.
Neural Networks 17.06. 30.06.
Deep Learning 24.6. 7.7.
Clustering 8.7. 14.07.
Bayesian Neural Networks 15.07. 21.07.
Variational Inference 22.07. 28.07.
Sampling 29.07. none

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

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News

03.04.2022

In April 2022 Jürgen Sturm, Christian Kerl and Daniel Cremers were featured among the top 10 most influential scholars in robotics of the last decade.

31.03.2022

We have open PhD and postdoc positions! To apply, please use our application form.

08.03.2022

We have six papers accepted to CVPR 2022 in New Orleans!

31.01.2022

We have two papers accepted to ICRA 2022 - congrats to Lukas von Stumberg, Qing Cheng and Niclas Zeller!

05.12.2021
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