10-601 Clustering

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This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016.



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