NME130/Graphical models

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Present: Andreas, Andy, Ufuk, Nader, Richard, John, Pablo, Javad


  • 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


  • 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)