CS C281B. Advanced Topics in Learning and Decision Making

Catalog Description: Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning.

Units: 3.0

Fall: 3 hours of lecture per week
Spring: 3 hours of lecture per week

Grading basis: letter

Final exam status: No final exam

Also listed as: STAT C241B

Class Schedule (Spring 2022):
TuTh 5:00PM - 6:29PM, Evans 332 – Martin Wainwright

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