Difference between revisions of "10-601 Clustering"
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| − | This a | + | This a pair of lectures used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]]. |
| + | |||
| + | There will be a guest lecturer, Bhavana Dalvi, lecturing on k-means on Monday and Thursday, and Ziv will lecture on agglomerative clustering and spectral clustering on Tuesday and Wednesday. | ||
| + | (So usually, the lectures will be in different orders to the two sections.) | ||
=== Slides === | === Slides === | ||
| − | [http://curtis.ml.cmu.edu/w/courses/images/a/a7/Lecture14-clustering.pdf Slides in PDF] | + | [http://curtis.ml.cmu.edu/w/courses/images/a/a7/Lecture14-clustering.pdf Slides in PDF] - draft |
=== Readings === | === Readings === | ||
Revision as of 12:41, 19 September 2014
This a pair of lectures used in the Syllabus for Machine Learning 10-601 in Fall 2014.
There will be a guest lecturer, Bhavana Dalvi, lecturing on k-means on Monday and Thursday, and Ziv will lecture on agglomerative clustering and spectral clustering on Tuesday and Wednesday. (So usually, the lectures will be in different orders to the two sections.)
Slides
Slides in PDF - draft
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
