# NME130/Graphical models

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Jump to navigationJump to searchPresent: 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)