Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Author: Joe Suzuki
Publisher Finelybook 出版社：Springer; 1st ed. 2022 edition (May 15, 2022)
pages 页数：220 pages
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 Author: considering relevant math problems and building Python 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 Author: 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.