## CS 228T: Advanced Topics in Probabilistic Graphical Models

*This is an archive of materials used for CS 228T, taught at Stanford in 2011 with Daphne Koller.*

**Course description**

An advanced course on probabilistic graphical models, covering advanced MCMC methods, variational inference, large margin methods, nonparametric Bayes, and other topics.

**Prerequisites**

The course requires CS 228 (probabilistic graphical models); CS 229 (machine learning) and EE 364A (convex optimization) are recommended.

**Syllabus**

- Advanced MCMC methods
- Variational inference
- MAP estimation
- Large margin methods
- Structure learning
- Latent variable models and topic models
- Bayesian nonparametrics

*The syllabus may be adjusted through the course of the semester.*

**Notes**

**Readings**

The textbook is Koller and Friedman, *Probabilistic Graphical Models*, and various research papers will be assigned throughout the semester.

**Homework**

**Quizzes**