10-601B Clustering
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Revision as of 09:26, 12 January 2016 by Wcohen (talk | contribs) (Created page with "This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016. === Slides === * ... === Readings === Mitchell 6.12 - a nice description o...")
This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016.
Slides
- ...
Readings
Mitchell 6.12 - a nice description of EM and k-means.
What You Should Know Afterward
You should know how to implement these methods, and what their relative advantages and disadvantages are.
- Overview of clustering
- Distance functions and similarity measures and their impact
- K-means algorithms
- How to chose k and what is the impact of large and small k's
- EM
- Differences between GM and K-means
