Computer Science 281B
| Title | Advanced Topics in Learning and Decision Making |
|---|---|
| Units | 3 |
| Prerequisites | C281A, Statistics C241A. |
| 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. Also listed as Statistics C241B. |
| Sections | Instructor | Teaching Effectiveness | How worthwhile was this course? |
|---|---|---|---|
| Spring 2016 | Peter Bartlett | ||
| Spring 2014 | Peter Bartlett | ||
| Spring 2013 | Elchanan Mossel | ||
| Spring 2009 | Martin Wainwright | ||
| Spring 2006 | Peter Bartlett | ||
| Spring 2004 | Michael Jordan | ||
| Spring 2003 | Peter Bartlett | ||
| Spring 2001 | Michael Jordan | ||
| Overall Rating | Teaching Effectiveness | How worthwhile was this course? | |