EAN: 9789811575679

STATISTICAL LEARNING WITH MATH AND R - JOE SUZU...

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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