10-601 Ensembles

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

Slides

Readings

  • Murphy 16.4, 16.6

Other optional readings:

Summary

You should understand how these ensemble methods work

  • Bagging
  • Stacking
  • Boosting

You should understand the basic intuitions behind the analysis of boosting:

  • As reducing an upper bound on error and hence fitting the training data.
  • As a coordinate descent optimization of the same upper bound.

You should also be aware that boosting is related to margin classifiers.