Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
- Author
- Joe Suzuki
- Publication Year
- 2022
- Publisher
- Springer
- Language
- English
- Document Type
- Book
- Faculty / Subject Heading
- Computer Science
- Download Book Read book
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: 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. / 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.
Keywords: Artificial Intelligence / Statistical Learning / Computational Intelligence / Data Science / Machine Learning / Kernel / Bayesian Statistics / Hilbert Space / Reproducing kernel Hilbert space / RKHS / Python
