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Statistical learning with math and R...

Suzuki, Joe.

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Statistical learning with math and R = 100 exercises for building logic /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical learning with math and R/ by Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xi, 217 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Textbooks. - Mathematics -
Online resource:	https://doi.org/10.1007/978-981-15-7568-6
ISBN:	9789811575686

Statistical learning with math and R = 100 exercises for building logic /
Suzuki, Joe.

Statistical learning with math and R100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Singapore :2020. - xi, 217 p. :ill., digital ;24 cm.

Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.

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.

ISBN: 9789811575686

Standard No.: 10.1007/978-981-15-7568-6doiSubjects--Topical Terms:

3526635
Machine learning
--Mathematics--Textbooks.

LC Class. No.: Q325.5 / .S89 2020

Dewey Class. No.: 006.3

Statistical learning with math and R = 100 exercises for building logic /
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505 0 $a Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
520 $a 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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