Brown CS 242

Probabilistic Graphical Models, Fall 2014.

Probabilistic graphical models provide a flexible framework for describing large, complex, heterogeneous collections of random variables. This course surveys state-of-the-art methods for statistical learning and inference in graphical models, as motivated by applications in image and video analysis, text and language processing, sensor networks, autonomous robotics, biological structure prediction, social networks, and more.

We will study efficient inference algorithms based on optimization-based variational methods, and simulation-based Monte Carlo methods. Several approaches to learning from data will be covered, including conditional models for discriminative learning, and Bayesian methods for controlling model complexity. Motivating applications will be explored via homework assignments and a final project. See the Fall 2014 syllabus for further details.


Office Hours

December 5, 2014

Regular office hours end on Friday, December 5. Additional office hours during reading period will be announced to the course mailing list.

Thanksgiving Week

November 24-28, 2014

During Thanksgiving week, the instructor and TA will be available for questions from 4:00-5:00pm on Tuesday, November 25. All other office hours are cancelled.

Project Proposals

November 3, 2014

The deadline for project proposals has been extended until Tuesday, November 11, 2014. See the detailed instructions.

Fall Weekend

October 13, 2014

Normal office hours on Monday, October 13 are cancelled due to the Fall Weekend Holiday. Prof. Sudderth will instead hold office hours on Wednesday, October 15 from 4:00-5:00pm.


September 2, 2014

The first lecture is on Thursday, September 4 at 2:30pm in CIT 368. After lecture, Prof. Sudderth will be available to answer questions about the course prerequisite, CS 142: Introduction to Machine Learning.