FREE SHIPPING BOTH WAYS

ON EVERY ORDER!

LIST PRICE:

$60.00

Sorry, this item is currently unavailable.

ISBN: 9780262012430 | 026201243X

Edition: 2ndFormat: Hardcover

Publisher: Mit Pr

Pub. Date: 2/26/2010

Because Knetbooks knows college students. Our rental program is designed to save you time and money. Whether you need a textbook for a semester, quarter or even a summer session, we have an option for you. Simply select a rental period, enter your information and your book will be on its way!

- We have the lowest prices on thousands of popular textbooks
- Free shipping both ways on ALL orders
- Most orders ship within 48 hours
- Need your book longer than expected? Extending your rental is simple
- Our customer support team is always here to help

**A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.**

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.

The **second edition of 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. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program.

The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden **Markov** models, assessing and comparing classification algorithms, and reinforcement learning.

New to the **second edition** are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises.

All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

"This volume offers a very accessible introduction to the field of machine learning. **Ethem Alpaydin** gives a comprehensive exposition of the kinds of modeling and prediction problems addressed by machine learning, as well as an overview of the most common families of paradigms, algorithms, and techniques in the field. The volume will be particularly useful to the newcomer eager to quickly get a grasp of the elements that compose this relatively new and rapidly evolving field."**-Joaquin Quiñonero-Candela, co-editor, Data-Set Shift in Machine Learning**