Probably Approximately Correct (PAC) learning is a foundational concept in the field of machine learning, offering a framework for understanding how learning algorithms can generalize from a finite set of training data to unseen instances. PAC learning theory addresses the feasibility of learning a function from examples, quantifying the number of training samples needed to ensure that the learned function will perform well on new, unseen examples with high probability. It provides a measure of how 'probably' (with high probability) a learning algorithm can find a hypothesis that is 'approximately correct' (with low error) on the training data.
Agnostic learning extends the PAC framework to scenarios where there may not exist a perfect hypothesis in the hypothesis class for the given data distribution. In agnostic learning, the goal is to find the best approximation to the true function within the hypothesis class. This is more realistic in many practical situations where the assumption of a perfect hypothesis is too strong and the underlying data may be noisy or imperfectly understood. Agnostic learning thus provides a more flexible and robust framework, acknowledging and accommodating the imperfections and uncertainties inherent in real-world data.
In this session, we will cover the concepts of PAC learning and Agnositic Learning of Finite Hypothesis Class.