| Lecture |
Date |
Topic |
Material |
| 1 |
28.10.2013 |
Introduction to Probabilistic Inference and Bayesian
Networks |
Slides |
| 2 |
04.11.2013
|
Probabilistic Inference in Bayesian Networks without
undirected cycles via Factor Graphs
|
Slides
|
| 3 |
18.11.2013
|
Markov Networks, and Comparison with Bayesian Networks
|
Slides
|
4
|
22.11.2013
|
The Junction Tree Algorithm for Probabilistic Inference in
arbitrary Graphical Models
|
Slides
|
5
|
25.11.2013
|
Learning as Probabilistic Inference
|
Slides
|
6
|
29.11.2013
|
Continuation: Learning as Probabilistic Inference
|
Slides
|
7
|
02.12.2013
|
Lagrange Multipliers and Decision Making
|
Slides
|
8
|
16.12.2013
|
Expectation Maximization: A Powerful Tool for Fitting
Complex Statistical Models to Data
|
Slides
|
9
|
13.01.2014
|
Completion of EM, and a few further useful Tricks and
Concepts in Machine Learning
|
Slides
|