Difference between revisions of "10-601 Clustering"
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Mitchell 6.12 also has a nice description of EM and k-means. | Mitchell 6.12 also has 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. | You should know how to implement these methods, and what their relative advantages and disadvantages are. | ||
* Overview of clustering | * Overview of clustering | ||
Revision as of 09:05, 12 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
Bishop's Chapter 9
Mitchell 6.12 also has 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
