Difference between revisions of "10-601 Clustering"

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Slides from Class on 13th October 2014 [[File:Kmeans_13october2014_dalvi.pptx]] Slides in PPT
 
Slides from Class on 13th October 2014 [[File:Kmeans_13october2014_dalvi.pptx]] Slides in PPT
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=== Readings ===
 
=== Readings ===
  

Revision as of 15:35, 13 October 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 from Fall 2013 Slides in PDF - draft

Slides from Class on 13th October 2014 File:Kmeans 13october2014 dalvi.pptx Slides in PPT

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