Information Theoretic Learning (ITL) is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings. This book deals with the ITL algorithms to adapt linear or nonlinear learning machines.