Description 
1 online resource (xi, 217 pages) : illustrations (some color) 
Contents 
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 
Summary 
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 easytofollow and selfcontained style, this book will also be perfect material for independent learning 
Notes 
Includes index 

Description based on resource, viewed January 4, 2021 
Subject 
Artificial intelligence  Mathematics  Textbooks


Logic, Symbolic and mathematical  Textbooks


Machine learning  Mathematics  Textbooks


R (Computer program language)  Textbooks


Artificial intelligence.


Machine learning.

Genre/Form 
Textbooks.

Form 
Electronic book

ISBN 
9789811575686 

9811575681 
