Probabilistic Graphical Models

Topics covered include:

1. The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities
2. Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
3. Learning parameters and structure in PGMs
4. Using a PGM for decision making under uncertainty.

There will be short weekly review quizzes and programming assignments (Octave/Matlab) focusing on case studies and applications of PGMs to real-world problems:

1. Credit Scoring and Factors
2. Modeling Genetic Inheritance and Disease
3. Markov Networks and Optical Character Recognition (OCR)
4. Inference: Belief Propagation
5. Markov Chain Monte Carlo and Image Segmentation
6. Decision Theory: Arrhythmogenic Right Ventricular Dysplasia
7. Conditional Random Field Learning for OCR
8. Structure Learning for Identifying Skeleton Structure
9. Human Action Recognition with Kinect

To prepare for the class in advance, you may consider reading through the following sections of the textbook (discount code DKPGM12) by Daphne and Nir Friedman:

1. Introduction and Overview. Chapters 1, 2.1.1 - 2.1.4, 4.2.1.
2. Bayesian Network Fundamentals. Chapters 3.1 - 3.3.
3. Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
4. Structured CPDs. Chapters 5.1 - 5.5.
5. Template Models. Chapters 6.1 - 6.4.1.

These will be covered in the first two weeks of the online class.

The slides for the whole class can be found here.

• Daphne Koller - School of Engineering