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 david.blei@columbia.edu.
- Introduction
- A Quick Review of Probability
- Basics of Graphical Models
- Reading #2: "Conditional Independence and Factorization" in
Introduction to Probabilistic Graphical Models (Jordan, 2003).
- Elimination, Tree
Propagation, and the Hidden Markov Model
- Reading #3: "The Elimination Algorithm" in Introduction to
Probabilistic Graphical Models (Jordan, 2003)
- Reading #4: "Probability Propagation and Factor Graphs" in
Introduction to Probabilistic Graphical Models (Jordan, 2003)
- Models, Data, and
Statistical Concepts
- Bayesian Mixture Models and the Gibbs Sampler
- Exponential Families
and Conjugate Priors
- Reading #7: "The Exponential Family" (Bishop, 2006; Section 2.4)
- Mixed-membership Models and Mean-Field Variational Inference
- Matrix Factorization and Recommendation Systems
- Generalized Linear Models
- Reading #11: "An outline of generalized linear models" in
Generalized Linear Models (McCullough and Nelder, 1989)
- Reading #11: "Multilevel linear structures" in Data Analysis Using Regression and Multilevel/Hierarchical Models
(Gelman and Hill, 2007)
- Reading #11: "Multilevel linear models: The basics" in Data Analysis Using Regression and Multilevel/Hierarchical Models
(Gelman and Hill, 2007)
Homework
Other materials