Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

Alpaydin, Ethem

ISBN 10: 0262028182 / ISBN 13: 9780262028189

Published by The MIT Press, 2014

About this Item

Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLYNO ACCESS CODE, NO CD, Ships with Emailed Tracking. Seller Inventory # SKU0487443

Bibliographic Details

Title: Introduction to Machine Learning (Adaptive ...

Publisher: The MIT Press

Publication Date: 2014

Binding: Hardcover

Condition: Good

Edition: 3rd Edition

About this title

Synopsis:

A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.

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. 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, semi-parametric, 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. The third edition of Introduction to Machine Learning 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 perceptrons 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 undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

About the Author:

Ethem Alpaydin is a Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul.