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Technical University of Munich

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

WS 2019, TU München

Announcements

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

Link for piazza: https://piazza.com/tum.de/fall2019/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 I", Hörsaal 2
Date: Fridays, starting from October 18th
Time: 16.00 - 18.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorials

Location: 2502, Physik Hörsaal 2 (5101.EG.502)
Date: Wednesdays, starting from October 30th
Time: 16.00 - 18.00
Tutors: Maximilian Denninger, Martin Sundermeyer, Max Durner, John Chiotellis

Location: 2501, Rudolf-Mößbauer-Hörsaal (5101.EG.501)
Date: Thursdays, starting from October 31st
Time: 16.00 - 18.00
Tutors: Maximilian Denninger, Martin Sundermeyer, Max Durner, John Chiotellis

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.: WS2018

Tentative Schedule
Topic Lecture Date Tutorial Dates
Introduction / Probabilistic Reasoning 18.10 30.10 and 31.10
Regression 25.10 6.11 and 7.11
Graphical Models 8.11 13.11 and 14.11
Bagging and Boosting 15.11 20.11 and 21.11
Metric Learning 22.11 27.11 and 28.11
Kernel Regression and Gaussian Processes 29.11 4.12 and 5.12
Gaussian Processes for Classification 6.12 11.12 and 12.12
Wrap-up Tutorial 18.12 and 19.12
Deep Learning 20.12 08.01 and 09.01
Clustering 1 10.01 15.01 and 16.01
Clustering 2 17.01 22.01 and 23.01
Sampling Methods 24.01 29.01 and 30.01
Variational Inference I 31.01 5.02 and 6.02
Variational Inference II 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 .

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

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