YoVDO

Characterizations of PAC Learnability

Offered By: Institute for Advanced Study via YouTube

Tags

Computational Learning Theory Courses PAC Learning Courses Sample Complexity Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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 Discussion
Simons 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