Difference between revisions of "10-601 Clustering"
From Cohen Courses
Jump to navigationJump to search| Line 10: | Line 10: | ||
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. | ||
| + | |||
| + | 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 | ||
Revision as of 09:04, 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.
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
