CS 281A / Stat 241A, Fall 2003:

Statistical Learning Theory

Text book:

An Introduction to Probabilistic Graphical Models, Michael I. Jordan:

Chapter 2. Conditional Independence and Factorization
Chapter 3. The Elimination Algorithm
Chapter 4. Probability Propagation and Factor Graphs
Chapter 5. Statistical Concepts
Chapter 6. Linear Regression and the LMS Algorithm
Chapter 7. Linear Classification
Chapter 8. The exponential family and generalized linear models
Chapter 9. Completely observed graphical models
Chapter 10. Mixtures and conditional mixtures
Chapter 11. The EM algorithm
Chapter 12. Hidden Markov models
Chapter 13. The multivariate Gaussian
Chapter 14. Factor analysis
Chapter 15. Kalman filtering and smoothing
Chapter 16. Markov Properties
Chapter 17. The junction tree algorithm
Chapter 18. The HMM and state space model revisited
Chapter 19. Features, maximum entropy and duality
Chapter 20. Iterative scaling algorithms
Chapter 21. Sampling methods
Chapter 29. Decision graphs

This is an unpublished manuscript. Please do not distribute these chapters. Also, please do not create links to this page on a public site. Thanks.