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TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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

SS 2021, TU München

Announcements

This semester, the lecture will be given online.

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

Link for piazza: https://piazza.com/tum.de/spring2021/in2357

FAQ

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

Lecture

Location: The lecture will be completely online.
Date: Fridays
Time: 14.00 - 16.00
Main Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: Online
Date: Wednesdays, starting from Apr 21
Time: 16.00 - 18.00
Lecturer: John Chiotellis, Maximilian Denninger, Martin Sundermeyer, Maximilian Durner
Jongseok Lee
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 Live lecture on BBB . 16.04. 21.04.
Regression Online lecture. See video here. There will be a Q+A session on BBB at the official lecture time. 23.04. 28.04.
Logistic Regression Live lecture on BBB . 30.04. 05.05.
Graphical Models Online lecture. See video here. There will be a Q+A session on BBB at the official lecture time. 07.05. 12.05.
Kernel Methods and Gaussian Processes I 14.05. 19.05.
Kernel Methods and Gaussian Processes II 21.05. 26.05.
Neural Networks 28.05. 02.06.
Deep Learning 04.06. 09.06.
Clustering 11.06. 16.06.
Bayesian Neural Networks 18.06. 23.06.
Variational Inference 25.06. 30.06.
Sampling 02.07. 07.07.
Repetition and Deepening 09.07. 14.07

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Informatik IX
Computer Vision Group

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

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