Computational Learning Theory (CLT) is a theoretical framework for understanding the fundamental principles of learning algorithms in terms of their efficiency and success in acquiring knowledge. It focuses on what types of problems can be learned from data, how much computational effort is required, and under what conditions learning is possible.