Foundations of Graphical Models
Fall 2018
Columbia University

Course information


Topics and readings

Below are the topics of the class and the readings. These topics may span multiple lectures. Some readings are not yet set, but this list will be kept up to date. The readings from Blei (2018) will be made available to the class.

  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. Time series and sequence models
  8. Exponential families, conjugate priors, and generalized linear models
  9. Hierarchical models, robust models, and empirical Bayes
  10. The theory of graphical models
  11. Advanced topics in variational inference
  12. Model checking and model diagnosis
  13. An introduction to causality