10-601 GM1

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This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016



  • Chapter 6.11 Mitchell
  • Chapter 10 Murphy
  • Or: Chap 8.1 and 8.2.2 (Bishop)
  • Or: Chap 15 (Russell and Norvig) - disclaimer, my edition is old!

To remember

  • Conditional independence and dependence
    • Notations for these
  • Semantics of a directed graphical model (aka Bayesian network, belief network)
    • Converting a joint probability distribution + conditional independencies to a network
    • Converting a network to a joint PDF
  • Determining conditional independencies from the structure of a network
    • Blocking
    • d-separation