Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With nearly 30 closed-form and iterative algorithms, the book provides a step-bystep guide to algorithmic procedures and analyzing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms and to build models for new application paradigms such as green IT and big data learning technologies. Numerous real-world examples and over 200 problems, several of which are MATLAB-based simulation exercises, make this an essential resource for undergraduate and graduate students in computer science, and in electrical and biomedical engineering. It is also a useful reference for researchers and practitioners in the field of machine learning. Solutions to some problems and additional resources are provided online for instructors.