IE598: Handouts

Sewoong Oh, University of Illinois Urbana-Champaign

The lecture notes will be updated as the course proceeds. Here is a brief summary of the course

  1. Overview (last updated 08.23.2016)

  2. Graphical models (last updated 08.23.2016)

  3. Markov property (last updated 08.31.2016)

  4. Elimination algorithm/Belief propagation (last updated 09.15.2016)

  5. Density evolution/LDPC code (last updated 09.15.2016)
    application: Crowdsourcing

  6. Max-product algorithm (last updated 09.15.2016)

  7. Gaussian graphical models (last updated 10.04.2016)

  8. Restricted Boltzmann machines (last updated 10.11.2016)

  9. Markov Chain Monte Carlo (last updated 10.20.2016)

  10. Variational inference (last updated 11.03.2016)

  11. Learning (last updated Fall 2012)

Final project

  • project description from 2015 Spring

  • Final Project for Fall 2016 is to read one of the following research papers and explain to the class in 25 minutes. You can use either the board or slides as you prefer. It will be nice to have a few pages of handout to share with the audience. The list of papers will be shared with the class on Piazza. Whoever claims a paper first in the reply gets it.


There are many notes that summarizes mostly intersecting and somewhat different areas in probabilistic graphical models. Depending on your background, some of the following references can prove to be extremely insightful.

Additional reading:

Markov properties

Belief propagation

Gaussian graphical models

Variational inference

Random processes on graphs