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 |