Brown CS 242

Probabilistic Graphical Models, Fall 2016.

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 2016 syllabus for further details.


Have Questions?

September 7, 2016

All students should sign up for the CS242 Piazza discussion site. Please watch Piazza for course announcements, and use it to post questions about homeworks and projects.

Please Register!

September 7, 2016

All students need to complete the CS242 registration survey. Without this, you won't be able to handin assignments or have them graded!