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Introduction to Machine Learning 3rd Edition by Ethem Alpaydin 2014
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A book that does not look new and has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections.
- Buy It NowIntroduction to Machine Learning 3rd Edition by Ethem Alpaydin 2014
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eBay item number:153943414470
Item specifics
- Condition
- Publisher
- MIT Press
- Subjects
- Computer & IT
- Format
- Hardcover
- Era
- 2010s
- Type
- Textbook
- Age Level
- Adults
- Topic
- Computing
- Publication Year
- 2014
- ISBN
- 9780262028189
- Subject Area
- Computers
- Publication Name
- Introduction to Machine Learning
- Item Length
- 8.9 in
- Subject
- Intelligence (Ai) & Semantics
- Series
- Adaptive Computation and Machine Learning Ser.
- Language
- English
- Item Height
- 1.1 in
- Item Weight
- 42.2 Oz
- Item Width
- 8.2 in
- Number of Pages
- 640 Pages
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262028182
ISBN-13
9780262028189
eBay Product ID (ePID)
202530623
Product Key Features
Number of Pages
640 Pages
Language
English
Publication Name
Introduction to Machine Learning
Subject
Intelligence (Ai) & Semantics
Publication Year
2014
Type
Textbook
Subject Area
Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
42.2 Oz
Item Length
8.9 in
Item Width
8.2 in
Additional Product Features
Edition Number
3
Intended Audience
College Audience
LCCN
2014-007214
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
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 Learnin g 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.
LC Classification Number
Q325.5.A46 2014
Item description from the seller
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