Location: Room 02.09.023
Date: Friday, starting at 25th October
Time: 9.15
Lecturer: Dr. Rudolph Triebel
ECTS: 4
SWS: 3

Location: Room 02.09.023
Date: Friday, starting at 8th November
Time: 14.15
Lecturer: Matthias Vestner

The next tutorial will be on Friday, 24th January

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. Also, in addition to the standard methods, the lecture will also cover some recent topics such as CRFs, Random Forests, and IVMs.

Schedule:
- Introduction
- Regression
- Probabilistic Graphical Models
- Boosting
- Kernel Methods
- Gaussian Processes
- Evaluation and Model Selection
- Sampling Methods
- Clustering

Slides of lectures 1-6
... with 8 slides per page

1. Introduction into Probabilistic Reasoning and Learning
2. Regression
3. Graphical Models I
4. Graphical Models II
5. Boosting
6. Kernel Methods
7. Gaussian Processes
8. Mixture Models and Expectation Maximization
9. Variational Inference
10. Sampling Methods I
11. Sampling Methods II
12. Clustering

2. Regression Solution Code
3. Graphical Models Solution
4. Boosting Code Solution
5. Kernel_Methods Code Solution
6. Kullback Leibler Divergence
7. Sampling Methods

The solutions for the last two excercises can be found in Bishop's solution book

Note that the video titles on YouTube start with index 1, while the lectures start with 2, so the video index is always 1 lower than the number of the lecture.