Introduction to Machine Learning
Language: English
Published by The MIT Press, 2014
- Hardcover
- Used

Seller: Anybook.com, Lincoln, United KingdomAnybook.com
AbeBooks seller since December 22, 1999
Condition: Used - Good
US$ 46.28
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Add to basketItem description from seller
This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1300grams, ISBN:9780262028189.
Seller Inventory # 7866884
- Title
- Introduction to Machine Learning
- Author
- Alpaydin, Ethem
- Publisher
- The MIT Press
- Publication year
- 2014
- Condition
- Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0262028182
- ISBN 13
- 9780262028189
- Edition
- 3rd Edition
- Item weight
- 1,300 grams
- Seller catalogs
- Engineering
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.
"Synopsis" may belong to another edition of this title.
About the Author
Ethem Alpaydin is a Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul.
"About the title" may belong to another edition of this title.
Anybook.com
Lincoln, United Kingdom
AbeBooks seller since December 22, 1999
Shipping rates from United Kingdom to U.S.A.
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