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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 .
