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.