Characterizations of PAC Learnability
Offered By: Institute for Advanced Study via YouTube
Course Description
Overview
Explore the concept of PAC (Probably Approximately Correct) learnability in this 15-minute talk by Nataly Brukhim, a postdoctoral member at the Institute for Advanced Study. Delve into the various characterizations of PAC learnability, a fundamental framework in computational learning theory. Gain insights into how this concept helps define the conditions under which a learning algorithm can reliably identify a target function within a specified error range. Understand the implications of PAC learnability for machine learning algorithms and their ability to generalize from training data to unseen examples.
Syllabus
Characterizations of PAC learnability - Nataly Brukhim
Taught by
Institute for Advanced Study
Related Courses
Beyond Worst-Case Analysis - Panel DiscussionSimons Institute via YouTube Reinforcement Learning - Part I
Simons Institute via YouTube Reinforcement Learning in Feature Space: Complexity and Regret
Simons Institute via YouTube Exploration with Limited Memory - Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits
Association for Computing Machinery (ACM) via YouTube Optimal Transport for Machine Learning - Gabriel Peyre, Ecole Normale Superieure
Alan Turing Institute via YouTube