Overview
- 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
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About this book
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.
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Keywords
Table of contents (6 chapters)
Authors and Affiliations
About the author
Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.
He is the author of a series of textbooks in machine learning published by Springer.
- Statistical Learning with Math and R
- Statistical Learning with Math and Python
- Sparse Estimation with Math and R
- Sparse Estimation with Math and Python
- Kernel Methods for Machine Learning with Math and R (This book)
- Kernel Methods for Machine Learning with Math and Python
He is the author of a series of textbooks in machine learning published by Springer.
- Statistical Learning with Math and R
- Statistical Learning with Math and Python
- Sparse Estimation with Math and R
- Sparse Estimation with Math and Python
- Kernel Methods for Machine Learning with Math and R (This book)
- Kernel Methods for Machine Learning with Math and Python
Bibliographic Information
Book Title: Kernel Methods for Machine Learning with Math and R
Book Subtitle: 100 Exercises for Building Logic
Authors: Joe Suzuki
DOI: https://doi.org/10.1007/978-981-19-0398-4
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Softcover ISBN: 978-981-19-0397-7Published: 04 May 2022
eBook ISBN: 978-981-19-0398-4Published: 04 May 2022
Edition Number: 1
Number of Pages: XII, 196
Number of Illustrations: 3 b/w illustrations, 29 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Statistics and Computing/Statistics Programs, Data Structures and Information Theory, Computational Intelligence