CS228 - Probabilistic Graphical Models: Principles and Techniques

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Career
Graduate
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
4
Course Repeatable for Degree Credit?
No

Course Component
Discussion
Enrollment Optional?
Yes
Course Component
Lecture
Enrollment Optional?
No