Foundations of Graphical Models
Fall 2015
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 Prof. Blei's office. The specific reading assignments are announced on Piazza.

These topics may span multiple lectures in the class. See the syllabus for the schedule. Note that these lecture notes are drafts and works in progress. Feel free to email with comments and errors.

  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. Probabilistic Modeling in Stan
  8. Exponential Families and Conjugate Priors
  9. Mixed-membership Models and Mean-Field Variational Inference
  10. Matrix Factorization and Recommendation Systems
  11. Generalized Linear Models
  12. Regularized Regression
  13. Bayesian Nonparametric Models

Homework assignments

  1. Homework 1
    Out: 2015-09-30
    Due: 2015-10-12

  2. Homework 2
    Out: 2015-10-20
    Due: 2015-11-04

Other materials