Joe Suzuki
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
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- More about Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Mathematical logic is essential for machine learning and data science, and this textbook provides a comprehensive introduction to kernel methods. It is written in an easy-to-follow style, includes 100 exercises, and demonstrates the mathematical premises of kernels through proven examples and running programs.
Format: Paperback / softback
Length: 208 pages
Publication date: 15 May 2022
Publisher: Springer Verlag, Singapore
The most essential skill for machine learning and data science is mathematical logic, which enables a deeper understanding of their core principles rather than relying solely on knowledge or experience. This comprehensive textbook delves into the fundamentals of kernel methods for machine learning, addressing relevant mathematical problems and developing Python programs. Key features of the book include:
Easy-to-follow and self-contained style: The content is presented in a clear and concise manner, making it accessible to readers with varying levels of mathematical background.
Comprehensive coverage: The book includes 100 carefully selected and refined exercises, providing readers with hands-on experience and opportunities to apply the concepts learned. Solutions to all exercises are provided in the main text, allowing readers to solve them independently.
Mathematical foundations: The mathematical premises of kernels are established, and the correct conclusions are derived, aiding readers in comprehending the nature of kernels.
Source programs and running examples: The book includes source programs and running examples to enhance readers' understanding of the mathematics used. These examples help clarify the theoretical concepts and provide practical insights.
Chapter-wise progression: The book begins with an introduction to functional analysis topics, Chapter 2, and then discusses applications in subsequent chapters. No prior knowledge of mathematics is assumed, allowing readers to build a solid foundation from scratch.
Difference between RKHS and Gaussian process: The book distinguishes between the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process, providing clear explanations and highlighting their distinct characteristics.
By mastering the principles outlined in this textbook, readers will gain a deep understanding of kernel methods and their applications in machine learning and data science. Whether you are a researcher, practitioner, or student, this resource will be invaluable in your pursuit of excellence in these fields.
Weight: 343g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811904004
Edition number: 1st ed. 2022
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