Introduction to Machine Learning / Edition 3

Introduction to Machine Learning / Edition 3

by Ethem Alpaydin
ISBN-10:
0262028182
ISBN-13:
9780262028189
Pub. Date:
08/31/2014
Publisher:
MIT Press
Select a Purchase Option (third edition)
  • purchase options
    $16.13 $65.00 Save 75%
    • Free return shipping at the end of the rental period details
    • Textbook Rentals in 3 Easy Steps  details
    icon-error
    Note: Access code and/or supplemental material are not guaranteed to be included with textbook rental or used textbook.
  • purchase options
    $57.77 $65.00 Save 11% Current price is $57.77, Original price is $65. You Save 11%.
  • purchase options
    $35.32 $65.00 Save 46% Current price is $35.32, Original price is $65. You Save 46%.
    icon-error
    Note: Access code and/or supplemental material are not guaranteed to be included with textbook rental or used textbook.
  • purchase options

Overview

Introduction to Machine Learning / Edition 3

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.

Product Details

ISBN-13: 9780262028189
Publisher: MIT Press
Publication date: 08/31/2014
Series: Adaptive Computation and Machine Learning series
Edition description: third edition
Pages: 640
Sales rank: 225,911
Product dimensions: 8.00(w) x 9.00(h) x 0.87(d)

Customer Reviews

Most Helpful Customer Reviews

See All Customer Reviews