Probabilistic Graphical Models: Principles and Techniques

CS
228
Instructors
Ermon, S. (PI)
Velu, A. (TA)
Cao, Z. (TA)
Cundy, C. (TA)
He, K. (TA)
Yan, X. (TA)
Section Number
1
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.
Grading
Letter or Credit/No Credit
Units
3-4
Graduate
Course Tags
Computational Policy - Electives
Computational Policy Analysis
Academic Year
Quarter
Winter
Section Days
Tuesday Thursday
Start Time
9:45 AM
End Time
11:15 AM
Location
NVIDIA Auditorium