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.
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.
News
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.
Discussion Group
October 14, 2014
The Google group brown.course.csci.2420.2014-fall.s01 may be used to discuss assignments and projects. All registered students are members.
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.
Mailing List
September 15, 2014
All registered students have been added to the primary course mailing list. Please confirm that you received the welcome message sent this afternoon, and that you regularly check e-mail sent to your CS account.
Welcome!
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.