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Faculty of Informatics
Technical University of Munich

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Home Teaching Winter Semester 2018/19 Machine Learning for Computer Vision (IN2357) (2h + 2h, 5ECTS)

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 10.12
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 builex3_pgms.pdfding 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
Exercises

Rechte Seite

Informatik IX
Chair for Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching

info@vision.in.tum.de