Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building

The Nile on
Kernel Methods for Machine Learning with Math and R by Joe Suzuki

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.

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Back Cover

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.

Table of Contents

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.

Feature

Equips readers with the logic required for machine learning and data science Provides in-depth understanding of source programs Written in an easy-to-follow and self-contained style

Details ISBN9811903972 Author Joe Suzuki Short Title Kernel Methods for Machine Learning with Math and R Language English Year 2022 ISBN-10 9811903972 ISBN-13 9789811903977 Format Paperback Subtitle 100 Exercises for Building Logic Publisher Springer Verlag, Singapore Edition 1st Imprint Springer Verlag, Singapore Place of Publication Singapore Country of Publication Singapore Pages 196 Publication Date 2022-05-04 UK Release Date 2022-05-04 Edition Description 1st ed. 2022 DEWEY 006.31 Country of Origin NL Product Class Description Computing: Professional & Programming Alternative 9789811903984 Illustrations 29 Illustrations, color; 3 Illustrations, black and white Audience Secondary
We\'ve got this

At The Nile, if you\'re looking for it, we\'ve got it.
With fast shipping, low prices, friendly service and well over a million items - you\'re bound to find what you want, at a price you\'ll love!

TheNile_Item_ID:135173212;