Foundations of Machine Learning • Part 2.1: PAC Learning Explained (Slides by Prof. Mohri, NYU)
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Foundations of Machine Learning • Part 2.1: PAC Learning Explained (Slides by Prof. Mohri, NYU)
In this video, we explore the PAC (Probably Approximately Correct) learning framework, which provides a formal foundation for machine learning. You will learn what it means for a learning algorithm to be “probably” and “approximately” correct, how to distinguish true error from empirical error, and why distribution-free learning is ...
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