Foundations of Graphical Models
Fall 2019
Columbia University

Course information


The course is open to all PhD students at Columbia University.


Please hand in all assignments through Courseworks.

Topics and readings

Below are the topics of the class and some readings about each. (Some readings are not yet set.)

The readings are at different levels: some are basic and some are advanced. We chose them to provide fundamental and other interesting material about the topics; the lectures will not necessarily cover or follow all of this material.

In addition to these readings, we will also make available Blei (2019), which is a book in preparation. The materials below are covered in this book.

  1. Introduction
  2. The ingredients of probabilistic models
  3. Bayesian mixture models and the Gibbs sampler
  4. Mixed-membership models, topic models, and variational inference
  5. Matrix factorization and efficient MAP inference
  6. Deep generative models and black box variational inference
  7. Exponential families, conjugate priors, and generalized linear models
  8. Hierarchical models, robust models, and empirical Bayes
  9. The theory of graphical models
  10. Advanced topics in variational inference
  11. Model checking and model diagnosis
  12. An introduction to causality