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PAC learning

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Machine learning allows computers to solve problems without being explicitly programmed with the solution. However, what sorts of problems can be learned? What does it even mean to learn a problem? The Probably Approximately Correct (PAC) machine learning framework addresses these questions, specifying worst case error bounds before a problem can be said to be learnable. In this talk, we will see a formulation of the supervised binary classification problem, followed by a definition of PAC learning. We will then try to find a way of determining which problems are PAC learnable, and which are not.

This talk is part of the Churchill CompSci Talks series.

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