Slides of the lectures

Lecture Date Topic Material
1 12.10.2015 Introduction to Probabilistic Inference and Bayesian Networks
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2 09.11.2015
Probabilistic Inference in Bayesian Networks without undirected cycles via Factor Graphs
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3 09.11.2015
Markov Networks, and Comparison with Bayesian Networks
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4
16.11.2015
The Junction Tree Algorithm for Probabilistic Inference in arbitrary Graphical Models
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5
23.11.2015
Learning as Probabilistic Inference
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6
30.11.2015
Further Concepts and Methods for Learning as Probabilistic Inference:
Exponential Family of Distributions, Bayesian Regression, Bayesian Decision Making, and Transfer Learning
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7
07.12.2015 Further Methods for Fitting Probability Distributions to Data:
Lagrange Multipliers and Expectation Maximization

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8
14.12.2015
Probabilistic inference through sampling
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9
11.01.2016
Speed-up of stochastic search and probabilistic inference through sampling
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10
18.01.2016 Learning as Stochastic Optimization and Methods for Integrating the Results of Different Learning Modules
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11
25.01.2016