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: d-separation, moral graph, practical considerations |
Slides: PDF |
|||
| Undirected graphical models |
Slides: PDF |
|||
| Exact inference: Variable elimination |
Slides: PDF |
|||
| Exact inference: Message passing in trees. Clique trees |
Slides: PDF |
|||
| Message passing in clique trees |
Slides: |
|||
| Junction trees |
Slides: |
|||
| Belief propagation in polytrees |
Slides: |
|||
| No class |
Slides: |
|||
| Loopy belief propagation |
Slides: |
|||
| Approximate inference: Likelihood weighting |
Slides: |
|
||
| Importance sampling and particle filters |
Slides: |
|||
| No class |
Slides: |
|||
| No class |
Slides: |
|||
| Approximate inference: Gibbs sampling. MCMC methods |
Slides: |
|
||
| Introduction to learning. Maximum likelihood |
Slides: |
|||
| Bayesian learning |
Slides: |
|
||
| Structure learning in Bayes nets with complete data |
Slides: |
|
||
| Expectation maximization |
Slides: |
|||
| More on expectation maximization. Applications to clustering |
Slides: |
|||
| No class |
Slides: |
|||
| Exponential family distributions |
Slides: |
|||
| Learning undirected graphical models |
Slides: |
|||
| Evaluation and comparison of different algorithms |
Slides: |
|||
| Hidden Markov models. Forward-backward inference algorithm |
Slides: |
|||
| Expectation maximization for learning HMMs. Dynamic Bayesian networks (DBN) |
Slides: |
|||
| Kalman filter and related models |
Slides: |
|||
| Introduction to decision making. Utility theory |
Slides: |
|
||
| Markov Decision Processes. Policies and value functions. Policy evaluation |
Slides: |
|||
| Optimal policies and value functions. Policy iteration and value iteration algorithms |
Slides: |
|||
| Monte Carlo and temporal difference learning for policy evaluation |
Slides: |
|||
| On-policy control learning: Sarsa. Exploration-exploitation trade-off |
Slides: |
|||
| Off-policy learning: Policy evaluation, Q-learning |
Slides: |
|||
| Actor-critic algorithms |
Slides: |
|||
| Partially Observable Markov Decision Processes (POMDPs). Planning methods for POMDPs |
Slides: |
|||
| More on planning for POMDPs. Learning methods for POMDPs |
Slides: |
|||
| Other methods for decision making under uncertainty |
Slides: |
|