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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