Preface

If you are new to machine learning and you do not know which book to start from, then the answer is this book. If you know some of the theories in machine learning, but you do not know how to write your own algorithms, then again you should start from this book.

This book focuses on the supervised and unsupervised machine learning methods. The main objective of this book is to introduce these methods in a simple and practical way, so that they can be understood even by beginners to get benefit from them.

In each chapter, we discuss the algorithms through which the chapter methods work, and implement the algorithms in MATLAB^®. We chose MATLAB to be the main programming language of the book because it is simple and widely used among scientists; at the same time, it supports the machine learning methods through its statistics toolbox.

The book consists of 12 chapters, divided into two sections:

   I: Supervised Learning Algorithms

 II: Unsupervised Learning Algorithms

In the first section, we discuss the decision trees, rule-based classifiers, naïve Bayes classification, k-nearest neighbors, neural networks, linear discriminant analysis, and support vector machines.

In the second section, we discuss the k-means, Gaussian mixture model, hidden Markov model, and principal component analysis in the context of dimensionality reduction.

We have written the chapters in such a way that all are independent of one another. That means the reader can start from any chapter and understand it easily.

MATLAB^® is a registered trademark of The MathWorks, Inc. For product information, please contact:

The MathWorks, Inc.

3 Apple Hill Drive

Natick, MA 01760-2098 USA

Tel: 508-647-7000

Fax: 508-647-7001

E-mail: [email protected]

Web: www.mathworks.com