Computational Learning Theory

What is Computational Learning Theory?

Computational Learning Theory (CoLT) is a field of AI research studying the design of machine learning algorithms to determine what sorts of problems are “learnable.” The ultimate goals are to understand the theoretical underpinnings of deep learning programs, what makes them work or not, while improving accuracy and efficiency.

This research field merges many disciplines, such as probability theory, statistics, programming optimization, information theory, calculus and geometry. 

Computational Learning Theory versus Statistical Learning Theory

While both frameworks use similar mathematical analysis, the primary difference between CoLT and SLT are their objectives. CoLT focuses on studying “learnability,” or what functions/features are necessary to make a given task learnable for an algorithm. Whereas SLT is primarily focused on studying and improving the accuracy of existing training programs.