Special Topics in Machine Learning: Probabilistic Graphical Models

David Sontag

Computer Science

Course website


This is a graduate-level course. Students should previously have taken one of the following classes: In addition, students should have a solid understanding of basic concepts from probability (e.g., Bayes' rule, multivariate distributions, conditional independence) and algorithms (e.g., dynamic programming, graphs, shortest paths, complexity).

These prerequisites may be waived in some cases (please e-mail instructor).

(Draft) Syllabus