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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)

WS 2020 / 2021, 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

Link for piazza: https://piazza.com/tum.de/winter2021/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: The lecture will be completely online.
Date: Fridays
Time: 16.00 - 18.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: Online
Date: Wednesdays and Thursdays, starting from Nov 11
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.: 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 . 06.11. 11.11.
Regression Online lecture. See video here. There will be a Q+A session on BBB at the official lecture time. 13.11. 18.11.
Logistic Regression Live lecture on BBB . 20.11. 25.11.
Graphical Models Online lecture. See video here. There will be a Q+A session on BBB at the official lecture time. 27.11. 02.12.
Kernel Methods and Gaussian Processes 04.12. 09.12.
Neural Networks 11.12. 16.12.
Deep Learning 18.12. 23.12.
Ensemble Learning 08.01. 13.01.
Bayesian Neural Networks 15.01. 20.01.
Clustering 22.01. 27.01.
Variational Inference 29.01. 03.02.
Sampling 05.02. 10.02.
Repetition and Deepening 12.02. none

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Informatik IX
Computer Vision Group

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

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17.07.2022

MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.

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