Statistical Learning with Math and R: 100 Exercises for Building Logic - Tapa blanda

Suzuki, Joe

 
9789811575679: Statistical Learning with Math and R: 100 Exercises for Building Logic
Tapa blanda
ISBN 10: 9811575673 ISBN 13: 9789811575679
Editorial: Springer, 2020

Sinopsis

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs.

As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning.

Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercisesin each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

 

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Acerca del autor

Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.

De la contraportada

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs.

As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning.

Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.


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Editorial
Springer
Año de publicación
2020
Idioma
Inglés
ISBN 10 
9811575673
ISBN 13 
9789811575679
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
232
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

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