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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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Informatik IX
Computer Vision Group

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

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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.

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

SS 2022, TU München

Announcements

This semester, the lecture will be given in presence. The lecture room is "102, Hörsaal 2, "Interims I" (5620.01.102)".

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

Link for piazza: https://piazza.com/tum.de/spring2022/in2357

FAQ

1. Attendance to the lecture is open for all students.

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 possibility to get extra points for your final grade, such as bonus exercises, etc.

Lecture

Location: Lecture hall Interims I (5620.01.102)
Date: Fridays, starting from May 6
Time: 12.00 - 14.00
Main Lecturer: PD Dr. habil. Rudolph Triebel
SWS: 2

Tutorial

Location: Lecture hall Interims I (5620.01.102)
Date: Thursday, starting from May 12
Time: 16.00 - 18.00
Lecturer: Dominik Schnaus
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.

For material from previous semesters, please refer to, e.g.: WS2019

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 .

Tentative Schedule
Topic Notes Lecture Date Tutorial Dates
Introduction / Probabilistic Reasoning Lecture in presence 6.05. 12.05.
Regression Lecture in presence 13.05. 19.05.
Logistic Regression Lecture in presence 20.05. 2.6.
Graphical Models Lecture in presence 27.05. 2.6.
Kernel Methods and Gaussian Processes I 3.6. 9.6.
Kernel Methods and Gaussian Processes II 10.06. 23.06.
Neural Networks 17.06. 30.06.
Deep Learning 24.6. 7.7.
Clustering 8.7. 14.07.
Bayesian Neural Networks 15.07. 21.07.
Variational Inference 22.07. 28.07.
Sampling 29.07. none

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