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

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

Grading basis: letter

Final exam status: No final exam

Also listed as: STAT C241B

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Class Schedule (Spring 2024):
CS C281B – MoWeFr 14:00-14:59, Tan 180 – Ryan Tibshirani

Class homepage on inst.eecs

Related Areas:

• Artificial Intelligence (AI)