NME130/Graphical models: Difference between revisions
From Murray Wiki
Jump to navigationJump to search
No edit summary |
No edit summary |
||
| Line 12: | Line 12: | ||
** Current research directions 3 lectures | ** Current research directions 3 lectures | ||
* Pre-requisites: machine learning (statistical models, generalization, distributions) | * Pre-requisites: machine learning (statistical models, generalization, distributions) | ||
=== Connections to NME 130 === | |||
* Information theory and coding (turbo codes, belief propogation) | |||
* Stochastic optimal control (MDPs, POMDPs -> controlled Markov chains, HMMS) | |||
* Dynamical systems (inference in hybrid systems, filtering) | |||
* Robustness (Bayesian model averaging, reasoning about very uncertain data) | |||
* Synthesis/hard limits (eg, how hard are certain decision problems) | |||
Module in 6 lectures (???) | |||
* GMs = probability/statistics meets algorithms/optimization | |||
* Modeling - 2 lectures: factorization, structured distributions, factor graphs | |||
* Inference - 2 lectures: inference as optimization, samping (Gibbs, MCMC) | |||
* Learning - 2 lectures: parameter, structure | |||
=== Discussion === | |||
* Pablo: much of the structure is purely algebraic; not really stochastic. Graphical structure allows you to be efficient. So lines up well with optimization. | |||
* John: what might make sense for next year - take a set of classes that are going to be taught anyway. Some subset of people take all of those courses together. Then an additional course that everyone takes together (CDS 212/213). | |||
** Stochastic modeling: ACM 116 (f), ACM 216 (w), EE 156 (f), CS 155 (w) | |||
** Optimization: ACM 113 (w) | |||
** Controls: CDS 210 (f) | |||
** Networking: CS 2xx (s) | |||
Revision as of 18:51, 19 June 2009
Present: Andreas, Andy, Ufuk, Nader, Richard, John, Pablo, Javad
CS/CNS/EE 155
- Course will be taught for the first time this fall; looking for feedback
- Key theme in graphical models: global insight from local observations
- Bayesian perspective; modeling, inference, learning
- Syllabus
- Modeling and representation - 5 lectures
- Inference - 6 lectures
- Learning GMs from data - 5 lectures
- Applications and case studies - 2 lectures
- Current research directions 3 lectures
- Pre-requisites: machine learning (statistical models, generalization, distributions)
Connections to NME 130
- Information theory and coding (turbo codes, belief propogation)
- Stochastic optimal control (MDPs, POMDPs -> controlled Markov chains, HMMS)
- Dynamical systems (inference in hybrid systems, filtering)
- Robustness (Bayesian model averaging, reasoning about very uncertain data)
- Synthesis/hard limits (eg, how hard are certain decision problems)
Module in 6 lectures (???)
- GMs = probability/statistics meets algorithms/optimization
- Modeling - 2 lectures: factorization, structured distributions, factor graphs
- Inference - 2 lectures: inference as optimization, samping (Gibbs, MCMC)
- Learning - 2 lectures: parameter, structure
Discussion
- Pablo: much of the structure is purely algebraic; not really stochastic. Graphical structure allows you to be efficient. So lines up well with optimization.
- John: what might make sense for next year - take a set of classes that are going to be taught anyway. Some subset of people take all of those courses together. Then an additional course that everyone takes together (CDS 212/213).
- Stochastic modeling: ACM 116 (f), ACM 216 (w), EE 156 (f), CS 155 (w)
- Optimization: ACM 113 (w)
- Controls: CDS 210 (f)
- Networking: CS 2xx (s)
