CSE 291: Bayesian methods

TuTh 11-12.30 in CSE 2154

Sanjoy Dasgupta
Office hours Tue 2-4 in CSE 4138

Administrative details

Course requirements: There will be periodic homework assignments as well as a final project.

The following textbooks contain a lot of the material we'll be covering:

Lecture schedule, homework assignments, and optional accompanying readings

  1. Course outline (Jan 8)

  2. Entropy, exponential families, and maximum likelihood (Jan 10,15,17,22)
  3. Homework 1, due 1/31.

  4. Bayesian inference for exponential families (Jan 24,29,31)
  5. Homework 2, due 2/12.

  6. Gaussian models: conditioning, linear regression, kernel trick, Bayesian model selection, Gaussian processes (Feb 5,7,19)
  7. Markov random fields: the Hammersley-Clifford theorem, Gibbs sampling, and MAP inference (Feb 21,26,28)
  8. Mixture models and Dirichlet processes (Mar 5,7)
  9. Topic models (Mar 12,14)
Projects are due Monday March 18.