10-601B Generalization and Overfitting: Sample Complexity Results for Supervised Classification

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

Slides

Readings

  • Mitchell Chapter 7

What you should remember

  • Distributional Learning Formulation.
  • Definition of sample complexity vs time complexity.
  • How sample complexity grows with 1/epsilon, 1/delta, and |H|...
    • in the noise free case.
    • in the "agnostic" setting, where noise is present and the learner outputs the smallest error-rate hypothesis.
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