Grundlagen des maschinellen Lernens für Predictive Data Analytics: Algorithmen,-

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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms,
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Artikelzustand
Sehr gut: Buch, das nicht neu aussieht und gelesen wurde, sich aber in einem hervorragenden Zustand ...
Pages
624
Publication Date
2015-07-24
Book Title
Fundamentals of Machine Learning for Predictive Data Analytics: A
ISBN
9780262029445
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
MIT Press
ISBN-10
0262029448
ISBN-13
9780262029445
eBay Product ID (ePID)
208620163

Product Key Features

Number of Pages
624 Pages
Publication Name
Fundamentals of Machine Learning for Predictive Data Analytics : Algorithms, Worked Examples, and Case Studies
Language
English
Subject
Probability & Statistics / Stochastic Processes, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2015
Type
Textbook
Subject Area
Mathematics, Computers
Author
Aoife D'arcy, Brian Mac Namee, John D. Kelleher
Format
Hardcover

Dimensions

Item Height
1.1 in
Item Weight
36.5 Oz
Item Length
9.2 in
Item Width
7.3 in

Additional Product Features

Intended Audience
Trade
LCCN
2014-046123
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals., A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning- information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
LC Classification Number
Q325.5.K455 2015

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