Difference between revisions of "10-601 PAC"

Revision as of 14:23, 9 October 2014

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

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 === What you should remember ===
-* Relationships between sample complexity, error bound, and “capacity” of chosen hypothesis space
+* Definition of pac-learnability.
-* Within the PAC learning setting, we can bound the probability that learner will output hypothesis with given error
+* Definition of sample complexity vs time complexity
-** For ANY consistent learner (case where c in H)
+* How sample complexity grows with 1/epsilon, 1/delta, and |H|
-** For ANY “best fit” hypothesis (agnostic learning, where perhaps c not in H)
+**  in the noise-free case.
+** in the "agnostic" setting, where noise is present and the learner outputs the smallest-error-rate hypothesis.
-* VC dimension as measure of complexity of H
+* The definition of VC-dimension and shattering
+* How VC dimension relates to sample complexity