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Courses in summer term 2010 / Lecture Bayesian Networks: Lecture Notes
Abstract
Lecture Notes
Exercises

The current lecture recordings and slides are available via learnweb / moodle [click here].
   0. Introduction[PDF]
   1. Basic Probability Calculus[PDF]
   2. Separation in Graphs[PDF]
   3. Bayesian and Markov Networks[PDF]
   4. Exact Inference / Variable Elimination[PDF]
   5. Exact Inference / Clustering[PDF]
   6. Exact Inference / Examples[PDF]
   7. Approximate Inference / Sampling[PDF]
   8. Approximate Inference / Adaptive Importance Sampling and Loopy Propagation[PDF]
   9. Learning Parameters (1-3)[PDF]
   10. Parameter Learning / Missing Values[PDF]
   11. Structure Learning / Constrained-based[PDF]

You will need the 'TechSmith Screen-Capture Codec (TSCC)' for playing the lecture notes as videos. The VLC-Player has it already integrated.

Lecture Notes as Videos:

Inhalt AVI Format
0. Overview [906 MB]
1. Basic Probability Calculus [240 MB]
2a. Separation in Graphs [642 MB]
2b. Separation in Graphs [703 MB]
3a. Bayesian and Markov Networks [192 MB]
3b. Bayesian and Markov Networks [928 MB]
3c. Bayesian and Markov Networks [863 MB]
3d. Bayesian and Markov Networks [364 MB]
4a. Exact Inference / Variable Elimination [483 MB]
4b. Exact Inference / Variable Elimination [903 MB]
5a. Exact Inference / Clustering [918 MB]
5b. Exact Inference / Clustering [903 MB]
6. Exact Inference / Examples [755 MB]
7. Approximate Inference [880 MB]
8. Approximate Inference [817 MB]
9. Learning Parameters [950 MB]
9b. Learning Parameters [525 MB]
10a. Parameter Learning / Missing Values [302 MB]
10b. Parameter Learning / Missing Values [848 MB]
11. Structure Learning [765 MB]