Difference between revisions of "10-601 Ensembles 1"

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This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]]
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=== Slides ===
 
=== Slides ===
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* [http://www.cs.cmu.edu/~zivbj/classF14/boosting.pdf Slides in pdf].
  
 
* [http://www.cs.cmu.edu/~wcohen/10-601/ensembles1.ppt Slides in PowerPoint].
 
* [http://www.cs.cmu.edu/~wcohen/10-601/ensembles1.ppt Slides in PowerPoint].
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* [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
 
* [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
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* [http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf A Short Introduction to Boosting], Yoav Freund and Robert Schapire.
  
=== Take home message ===
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=== Summary  ===
 
 
* Overfitting
 
** kNN
 
** Regression
 
 
 
* Bias-variance decomposition
 
 
 
* Structural risk minimization
 
  
* The battle against overfitting
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You should know how to implement  these ensemble methods, and what their relative advantages and disadvantages are:
** Cross validation
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* (Ziv - not sure if can do in one lecture if we do boosting) Bagging
** Regularization
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* Boosting
** Feature selection
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* Stacking
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* Multilevel Stacking
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* (Ziv - not sure if I will do this) The "bucket of models" classifier
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* Random forest

Latest revision as of 07:53, 22 October 2014

This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014

Slides

Readings

Summary

You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are:

  • (Ziv - not sure if can do in one lecture if we do boosting) Bagging
  • Boosting
  • Stacking
  • Multilevel Stacking
  • (Ziv - not sure if I will do this) The "bucket of models" classifier
  • Random forest