Difference between revisions of "10-601 GM1"
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* Conditional independence and dependence | * Conditional independence and dependence | ||
| + | ** Notations for these | ||
* Semantics of a directed graphical model (aka Bayesian network, belief network) | * Semantics of a directed graphical model (aka Bayesian network, belief network) | ||
** Converting a joint probability distribution + conditional independencies to a network | ** Converting a joint probability distribution + conditional independencies to a network | ||
Revision as of 09:15, 21 March 2016
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- Chapter 6.11 Mitchell
- Chapter 10 Murphy
- Or: Chap 8.1 and 8.2.2 (Bishop)
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
