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

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

SS 2020, TU München

Announcements

This semester, the lecture will be given partly online. This means that several topics will be made available from an earlier recording of the lecture. A detailed lecture plan will be given on this page.

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

April, 24th: Link for piazza: https://piazza.com/tum.de/spring2020/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 way to get extra points for your final grade, such as bonus exercises, etc.

Lecture

Location: For now, the lecture will be online. Later in the semester, we will be in 5620.01.102 Interims Hörsaal 2
Date: Fridays
Time: 12.00 - 14.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: For now online, later in 620.01.102 Interims Hörsaal 2
Date: Thursdays, starting from May 7th
Time: 16.00 - 18.00
Lecturer: John Chiotellis, Maximilian Denninger, Martin Sundermeyer, Maximilian Durner
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.: WS2017

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 No lecture! Please find introductory slides here 24.04. 07.05.
Regression Online lecture. See video here. 08.05. 14.05.
Graphical Models Online lecture. See video here . 15.05. 21.05.
Boosting Online lecture. See video here . Note that there is a "-" sign missing in the derivation on the board. This is a mistake which is corrected later in the video. 22.05. 28.05.
Kernel Methods Online lecture. See video here . 29.05. 04.06.
Gaussian Processes Online lecture. See video here 05.06. 18.06.
Metric Learning 12.6 25.6.
Gaussian Mixture Models and EM (Clustering I) 19.6. 2.7
Clustering II 26.6. none
Deep Learning 3.7. 9.7.
Variational Inference I 10.7. 16.7.
Variational Inference II 17.7. 23.7.
Sampling Methods 24.7. none

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Computer Vision Group

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

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