Stat 241b: Advanced Topics in Learning and Decision Making
UC Berkeley
Offerings
Overview
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.
Logistics
Three hours of Lecture per week for 15 weeks.