Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semiparametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical  models;  Bayesian  estimation;  and  statistical  testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. This new edition of the book reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptors and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students  can  easily  move  from  the  equations  in  the  book  to  a  computer  program. 

The book can be used by both advanced undergraduate and postgraduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

“Ethem Alpaydin's Introduction to Machine Learning provides a nice blending of the topical coverage of machine learning (à la Tom Mitchell) with formal probabilistic foundations (à la Christopher Bishop). This newly updated version now introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and learning to rank) to students and researchers of this critically  important  and  expanding  field.”

—John W. Sheppard, Professor of Computer Science, Montana State University

“This volume is both a complete and accessible introduction to the machine learning world. This is a 'Swiss Army knife' book for this rapidly evolving subject. Although intended as an introduction, it will be useful not only for students but for any professional looking for a comprehensive book in this field. Newcomers will find clearly explained concepts and experts will find a source for new references and ideas.”

—Hilario Gómez-Moreno, IEEE Senior Member, University of Alcalá, Spain

Preface
Notations
1.   Introduction
2    Supervised Learning
3.   Bayesian Decision Theory
4.   Parametric Methods
5.   Multivariate Methods
6.   Dimensionality Reduction
7.   Clustering
8.   Nonparametric Methods
9.   Decision Trees
10. Linear Discrimination
11. Multilayer Perceptrons
12. Local Models
13. Kernel Machines
14. Graphical Models
15. Hidden Markov Models
16. Bayesian Estimation
17. Combining Multiple Learners
18. Reinforcement Learning
19. Design and Analysis of Machine Learning Experiments
A. Probability
Index