Difference between revisions of "10-601 SSL"

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This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]
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=== Slides ===
 
=== Slides ===
  
* [http://www.cs.cmu.edu/~wcohen/10-601/cf.pptx Slides in PowerPoint].
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* [http://www.cs.cmu.edu/~wcohen/10-601/ssl.pptx Slides in PowerPoint].
  
 
=== Readings ===
 
=== Readings ===
  
* This is not covered in Mitchell.   
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* This is not covered in Mitchell.  An optional reading is an excellent short textbook by Jerry Zhu: [http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006 Introduction to Semi-Supervised Learning Synthesis Lectures on Artificial Intelligence and Machine Learning], Chapters 1-3 + 5.  This is a free PDF if you're on the CMU network.
  
 
===  Summary  ===
 
===  Summary  ===
  
 
You should know:
 
You should know:
* What collaborative filtering is.
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* What semi-supervised learning is - i.e., what the inputs and outputs are.
* How nearest-neighbor methods for CF work.
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* How K-means and mixture-models can be extended to perform SSL.
* How to formulate CF as a regression or classification problem.
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* The difference between transductive and inductive semi-supervised learning.
* How matrix factorization can be used for CF.
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* What graph-based SSL is.
* How PCA, SVD, k-means, and other clustering methods relate to matrix factorization.
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* The definition/implementation of the harmonic function SSL method (variously called wvRN, HF, co-EM, ...)

Latest revision as of 16:51, 6 January 2016

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

Readings

Summary

You should know:

  • What semi-supervised learning is - i.e., what the inputs and outputs are.
  • How K-means and mixture-models can be extended to perform SSL.
  • The difference between transductive and inductive semi-supervised learning.
  • What graph-based SSL is.
  • The definition/implementation of the harmonic function SSL method (variously called wvRN, HF, co-EM, ...)