Joe Suzuki
Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic
Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic
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- More about Kernel Methods for Machine Learning with Math and R: 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 with 100 exercises and proven mathematical premises. It covers the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process, and no prior knowledge of mathematics is assumed.
Format: Paperback / softback
Length: 196 pages
Publication date: 04 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 textbook delves into the fundamentals of kernel methods for machine learning by addressing relevant mathematical problems and developing R programs. The key features of the book are as follows:
The content is presented in a clear and concise manner, making it accessible to readers with varying levels of expertise.
The book includes a comprehensive set of 100 exercises, carefully selected and refined to enhance understanding and application of the material. Solutions to all exercises are provided in the main text, allowing readers to solve them by simply reading through the book.
The mathematical foundations of kernels are established, with correct conclusions provided to aid readers in comprehending the nature of kernels.
Source programs and running examples are included to facilitate a deeper understanding of the mathematics employed.
After gaining a basic understanding of the functional analysis topics covered in Chapter 2, the book discusses applications in subsequent chapters. No prior knowledge of mathematics is assumed, making it suitable for a wide range of readers.
Furthermore, the book distinguishes between the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process, providing clear explanations of their differences and applications.
By leveraging mathematical logic and kernel methods, this textbook offers valuable insights into machine learning and data science, empowering readers to analyze and interpret complex data with greater efficiency and accuracy.
Weight: 326g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811903977
Edition number: 1st ed. 2022
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