Computational learning theory explores how computers can efficiently learn from data. The authors introduce key concepts in this field, aiming to uncover the methods behind effective learning algorithms and identify obstacles to learning. They cover topics like the Valiant model for learning, Occam's Razor for data compression, and the Vapnik-Chervonenkis dimension. The research shows the relationship between learning and cryptography, the limits on efficient learning, and how to learn finite automata through active experimentation.