All the lecture notes are linked to this web page, in PDF format, with 2 slides per page. The reader for PDF files is available free from Adobe for UNIX, Apple Macintosh, and Windows.
I am happy to provde source files for all these slides upon request for teaching purposes.
Introduction 
RN Chapter 13 Slides: PDF 

Conditional independence. Introduction to belief networks 
RN, Sec. 14.1, 14.2; MJ, Sec. 2.1 Slides: PDF 

Independence maps. Bayes ball algorithm 
Slides: PDF 


More on Bayes nets: dseparation, moral graph, practical considerations 
Slides: PDF 

Undirected graphical models 
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Exact inference: Variable elimination 
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Exact inference: Message passing in trees. Clique trees 
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Message passing in clique trees 
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Junction trees 
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Belief propagation in polytrees 
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No class 
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Loopy belief propagation 
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Approximate inference: Likelihood weighting 
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Importance sampling and particle filters 
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No class 
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No class 
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Approximate inference: Gibbs sampling. MCMC methods 
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Introduction to learning. Maximum likelihood 
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Bayesian learning 
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Structure learning in Bayes nets with complete data 
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Expectation maximization 
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More on expectation maximization. Applications to clustering 
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No class 
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Exponential family distributions 
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Learning undirected graphical models 
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Evaluation and comparison of different algorithms 
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Hidden Markov models. Forwardbackward inference algorithm 
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Expectation maximization for learning HMMs. Dynamic Bayesian networks (DBN) 
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Kalman filter and related models 
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Introduction to decision making. Utility theory 
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Markov Decision Processes. Policies and value functions. Policy evaluation 
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Optimal policies and value functions. Policy iteration and value iteration algorithms 
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Monte Carlo and temporal difference learning for policy evaluation 
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Onpolicy control learning: Sarsa. Explorationexploitation tradeoff 
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Offpolicy learning: Policy evaluation, Qlearning 
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Actorcritic algorithms 
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Partially Observable Markov Decision Processes (POMDPs). Planning methods for POMDPs 
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More on planning for POMDPs. Learning methods for POMDPs 
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Other methods for decision making under uncertainty 
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