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TUM Department of Informatics
Technical University of Munich

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

WS 2018, TU München

Announcements

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

October, 12th: First tutorial will be on November 8th.

November, 6th: Link for piazza: https://piazza.com/ tum.de/fall2018/ 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: 5620.01.102 Interims Hörsaal 2
Date: Fridays, starting from October 19th
Time: 16.00 - 18.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: 2501 (Hörsaal 1), Building: 5101 Physik I
Date: Thursdays, starting from November 8th
Time: 16.00 - 18.00
Lecturer: John Chiotellis, Maximilian Denninger
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.

Note that the lecture has a new module number now. In earlier semesters it was IN3200, now it is IN2357. The content is however (almost) the same. For material from previous semesters, please refer to, e.g.: WS2017

Tentative Schedule
Topic Lecture Date Tutorial Date
Introduction / Probabilistic Reasoning 19.10 08.11
Regression 26.10 08.11
Graphical Models I 02.11 08.11
Graphical Models II 09.11 15.11
Bagging and Boosting 16.11 22.11
Metric Learning 23.11 29.11
Kernel Regression and Gaussian Processes 30.11 6.12
Deep Learning 7.12 13.12
Gaussian Processes for Classification 14.12 20.12
Clustering 1 21.12 10.01
Clustering 2 11.01 17.01
Variational Inference I 18.01 24.01
Variational Inference II 25.01 31.01
Sampling Methods 01.02 07.02

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 .

Lecture Slides

Rechte Seite

Informatik IX
Computer Vision Group

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

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News

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.

17.07.2022

AI Symposium

On July 22nd 2022, we are organizing a Symposium on AI within the Technology Forum of the Bavarian Academy of Sciences.

05.07.2022

We are organizing a workshop on Map-Based Localization for Autonomous Driving at ECCV 2022, Tel Aviv, Israel.

03.04.2022

In April 2022 Jürgen Sturm and Daniel Cremers were featured among the top 6 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.

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