computational learning theory

Computational Learning Theory is a field of study that explores how machines can learn from data. It examines the algorithms that enable computers to recognize patterns, make predictions, and improve over time with experience. The theory provides a framework for understanding what can be learned, how to evaluate learning effectiveness, and the limits of computation. It combines insights from computer science, statistics, and cognitive science, aiming to understand not just how to build learning systems, but also the fundamental principles that govern their behavior and performance in various tasks.