Class 1
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Introduction
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Class 2
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Probability Theory
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Class 3
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Graph Theory
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Class 4
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Bayesian Classifiers
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Class 5
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Hidden Markov Models
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Class 6
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Markov Random Fields
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Class 7
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Bayesian networks: representation and inference
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Class 8
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Bayesian networks: learning
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Class 9
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Mid Term Exam
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Class 10
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Project proposals / Dynamic and Temporal Bayesian networks |
Class 11
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Decision graphs
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Class 12
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MDPs |
Class 13
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Relational probabilistic graphical models
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Class 14
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Causal models / Final project - preliminary report
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Class 15
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Final project - presentation and final report
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