Kernel methods for machine learning with Math and Python : 100 exercises for building logic / Joe Suzuki.
Suzuki, Joe, author.
2022
QA353.K47
Available Online

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Title Kernel methods for machine learning with Math and Python : 100 exercises for building logic / Joe Suzuki.
Author Suzuki, Joe, author.
ISBN 9789811904011 (electronic bk.)
9811904014 (electronic bk.)
9789811904004 (print)
9811904006
DOI https://doi.org/10.1007/978-981-19-0401-1
Published Singapore : Springer, 2022.
Language English
Description 1 online resource (xii, 208 pages) : illustrations (some color)
Other Standard Identifiers 10.1007/978-981-19-0401-1 doi
Call Number QA353.K47
Dewey Decimal Classification 515/.9
Summary 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 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 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 23, 2022).
Available in Other Form Print version: 9789811904004
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Record Appears in Online Resources > Ebooks
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Chapter 1: Positive Definite Kernels
Chapter 2: Hilbert Spaces
Chapter 3: Reproducing Kernel Hilbert Space
Chapter 4: Kernel Computations
Chapter 5: MMD and HSIC
Chapter 6: Gaussian Processes and Functional Data Analyses.

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