Difference between revisions of "10-601 GM1"
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=== Readings === | === Readings === | ||
| − | * Chap 8.1 and 8.2.2 (Bishop) | + | |
| − | + | * Chapter 6.11 Mitchell | |
| + | * Chapter 10 Murphy | ||
| + | |||
| + | * Or: Chap 8.1 and 8.2.2 (Bishop) | ||
=== Taking home message === | === Taking home message === | ||
Revision as of 10:12, 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)
Taking home message
- factorization theorem of BN
- Full, independent and intermediate conditional probability models
- Markov blanket
- Learning a BN
- Inference in BN is NP hard
- Approximate inference in BN
