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TUM School of Computation, Information and Technology
Technical University of Munich

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Informatik IX
Computer Vision Group

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

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04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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This is an old revision of the document!


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

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More