10-601 PAC
From Cohen Courses
Revision as of 14:18, 9 October 2014 by Wcohen (talk | contribs) (→An example of PAC learnability of Boolean literals : Learning a Boolean Conjunction)
Slides
- Ziv's lecture: Slides in pdf.
- William's lecture: Slides in pdf, Slides in Powerpoint,
Readings
- Mitchell Chapter 7
What you should remember
- Relationships between sample complexity, error bound, and “capacity” of chosen hypothesis space
- Within the PAC learning setting, we can bound the probability that learner will output hypothesis with given error
- For ANY consistent learner (case where c in H)
- For ANY “best fit” hypothesis (agnostic learning, where perhaps c not in H)
- VC dimension as measure of complexity of H
