Difference between revisions of "10-601 GM1"
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=== Slides === | === Slides === | ||
| − | * | + | * [http://www.cs.cmu.edu/~wcohen/10-601/networks-1.pdf Slides in pdf], [http://www.cs.cmu.edu/~wcohen/10-601/networks-1.pptx Slides in PPT]. |
=== Readings === | === Readings === | ||
Revision as of 09:10, 21 March 2016
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- Chap 8.1 and 8.2.2 (Bishop)
- Graphical Models by Michael I. Jordan
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
