Difference between revisions of "10-601 Clustering"

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This a pair of lectures used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]].   
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This a pair of lectures used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]].   
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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.
 
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(So usually, the lectures will be in different orders to the two sections.)
 
  
 
=== Slides ===
 
=== Slides ===
  
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Slides from Fall 2013 [http://curtis.ml.cmu.edu/w/courses/images/a/a7/Lecture14-clustering.pdf Slides in PDF] - draft
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* Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/clusteringH.pdf Slides in pdf].
  
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Slides from Class on 13th October 2014 [[File:Kmeans_13october2014_dalvi.pptx]] Slides in PPT
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* Bhavana's lecture on 13th October 2014 [[Media:Kmeans_13october2014_dalvi.pptx]] Slides in PPT
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* Bhavana's lecture on 16th October 2014 [[Media:Kmeans_16october2014_dalvi.pptx]] Slides in PPT
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* Combined PDF version for classes on 13th and 16th October [[Media:Kmeans_cs601_CMU_dalvi.pdf]] Slides in PDF (Only the slides on advanced topics vary across lectures)
  
 
=== Readings ===
 
=== Readings ===
  
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Bishop's Chapter 9
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Mitchell 6.12 -  a nice description of EM and k-means.
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Mitchell 6.12 also has a nice description of EM and k-means.
 
  
 
=== What You Should Know Afterward ===
 
=== What You Should Know Afterward ===

Latest revision as of 15:49, 6 January 2016

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

Slides

Readings

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
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