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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Machine Learning for Robotics and Computer Vision (IN3200) (2h + 2h, 5ECTS)

WS 2017, TU München

Announcements

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

Beginning from Monday, 13.11.2017, the tutorial will be taking place in Room 00.08.059.

There is no tutorial on Monday, 20.11.2017.

Exam

Some students noted that their exam registration status in TUMonline is: "registered (preliminary registration)" with an exclamation mark in yellow circle. As far as we know that does not affect you. You can come to the exam.

No cheatsheets, calculators or other assistances are allowed.

There is NO repeat exam. The course is offered again in the next semester.

Lecture

Location: CH 27402, Walter-Hieber-Hörsaal (5407.01.740B)
Date: Fridays, starting from October 20th
Time: 10.15 - 12.00
Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: 00.08.059 NEW!
Date: Mondays, starting from October 23rd
Time: 14.00 - 16.00
Lecturer: John Chiotellis, Maximilian Denninger
SWS: 2
Office hours: Wednesdays, 13.30 - 14.30

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.

Tentative Schedule
Topic Lecture Date Tutorial Date
Introduction / Probabilistic Reasoning 20.10 23.10 and 30.10
Regression 27.10 6.11
Graphical Models (directed) 3.11 13.11
Graphical Models (undirected) 10.11 20.11
Metric Learning 17.11 27.11
Bagging and Boosting 24.11 4.12
Sequential Data / Hidden Markov Models 1.12 11.12
Kernels and Gaussian Processes 8.12 18.12
Deep Learning 15.12 15.1
Clustering 1 12.1 22.1
Clustering 2 19.1 29.1
Variational Inference 1 26.1 5.2
Variational Inference 2 2.2 5.2
Sampling Methods 9.2 12.2

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
Exercises

Rechte Seite

Informatik IX
Computer Vision Group

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

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News

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023.

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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.

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