The most crucial ability for machine learning and data science is mathematical logic for
grasping their essence rather than relying on knowledge or experience. This textbook addresses
the fundamentals of kernel methods for machine learning by considering relevant math problems
and building R programs. The book's main features are as follows: The content is written in an
easy-to-follow and self-contained style. The book includes 100 exercises which have been
carefully selected and refined. As their solutions are provided in the main text readers can
solve all of the exercises by reading the book. The mathematical premises of kernels are proven
and the correct conclusions are provided helping readers to understand the nature of kernels.
Source programs and running examples are presented to help readers acquire a deeper
understanding of the mathematics used. Once readers have a basic understanding of the
functional analysis topics covered in Chapter 2 the applications are discussed in the
subsequent chapters. Here no prior knowledge of mathematics is assumed. This book considers
both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian
process a clear distinction is made between the two.
