Slides of the lectures

Lecture Date Topic Material
1 10.10.2011 (updated on 17.10.2011) Introduction to Probabilistic Inference and Bayesian Networks Slides
2 17.10.2011 Probabilistic Inference in Bayesian Networks without undirected cycles via Factor Graphs Slides
3 24.10.2011 Probabilistic Inference in Bayesian Networks without undirected cycles via Factor Graphs Slides
    Application of Probabilistic Inference in Machine Learning Slides
4 31.10.2011 Further Applications of Probability Theory in Machine Learning Slides
5 07.11.2011 Further Applications of Probability Theory in Machine Learning: Using Continuous Random Variables Slides
6 14.11.2011 Applications of Probability Theory in Robotics
Slides
7 21.11.2011 Structure Learning in Bayesian Networks
Slides
8 5.12.2011 Expectation Maximization: The Main Tool for Fitting Complex Statistical Models to Data
Slides
9
9.1.2012
Undirected Graphical Models for Probability Distributions
Slides
10
16.1.2012
1. Another Method for Probabilistic Inference: MCMC Sampling
2. Parameter Learning in Bayesian Networks
Slides