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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
1. Introduction and Probabilistic Reasoning
2. Regression: MLE and MAP
3. Regression \ Directed Graphical Models
4. Undirected Graphical Models
5. Metric Learning
6. Boosting and Bagging
7. HMMs for Sequential Data
8. Kernel Methods and Gaussian Processes
9. Deep Learning
10. GP continued and Clustering I (EM)
11. Clustering II
12. Variational Inference I
13. Variational Inference II (EP)/ Sampling I
14. Sampling II (MCMC)
