PAC Learning
Probably Approximately Correct learning is a mathematical framework that defines the conditions under which a machine learning algorithm will reliably succeed.
Think of It Like This
Like mathematically proving that if you ask enough random people for directions, you will almost certainly end up very close to your destination.
PAC learning formalizes the trade-off between the complexity of a hypothesis space, the amount of training data, and the confidence of the model's accuracy. It guarantees that with high probability, the model will output a hypothesis that has a low generalization error. It forms the theoretical bedrock of modern machine learning theory.