Foundations of Graphical Models
Fall 2016
Columbia University

Course information

Course materials

Below are the readings and lecture notes. When available, we include a link to the PDF of the readings. Otherwise, they are available outside of D. Blei's office (912 SSW). These topics may span multiple lectures in the class.

The lecture notes are works in progress. If you have comments about them or notice errors, please email

  1. Introduction
  2. A Quick Review of Probability
  3. Basics of Graphical Models
  4. Elimination, Tree Propagation, and the Hidden Markov Model
  5. Models, Data, and Statistical Concepts
  6. Bayesian Mixture Models and the Gibbs Sampler
  7. Exponential Families and Conjugate Priors
  8. Mixed-membership Models and Mean-Field Variational Inference
  9. Matrix Factorization and Recommendation Systems
  10. Generalized Linear Models


Other materials