10-601 PAC

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



  • Mitchell Chapter 7

What you should remember

  • Definition of pac-learnability.
  • 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.
  • The definition of VC-dimension and shattering
  • How VC dimension relates to sample complexity