10-601 Clustering
From Cohen Courses
Jump to navigationJump to searchThis 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
