 Probably approximately correct learning

Probably Approximately Correct (PAC) learning is a framework in computational learning theory that formalizes the concept of learning from examples. Introduced by Leslie Valiant in 1984, PAC learning provides a mathematical foundation for understanding how well a learning algorithm can generalize from a finite set of training data to unseen data. ### Key Concepts: 1. **Hypothesis Space**: This is the set of all possible hypotheses (or models) that a learning algorithm can consider.