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Text · Engelska
Introduction to machine learning
Ethem Alpaydin
ISBN 0262325748
Third edition.
utgivning
Cambridge, Massachusetts : MIT Press, 2014, [2014]1 online resource (xxii, 616 pages)
Seriemedlemskap
Adaptive computation and machine learning · Adaptive computation and machine learning seriesEthem Alpaydin
Onlineresurs
Tillgänglighet utifrån medietyp
ämne
Machine learning.Annat bärarformat
Introduction to machine learning / · ISBN 0-262-02818-2 (Print:)Sammanfattning
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.
Innehållsförteckning
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Detaljer
Medverkan och funktion
Ethem Alpaydinklassifikation
006.3/1 (DDK-klassifikation)Identifikator
ISBN 0262325748Indirekt identifierad av
ISBN 0262028182har titel
Introduction to machine learningupphovsuppgift
Ethem Alpaydinupplageuppgift
Third edition.utgivning
Cambridge, Massachusetts : MIT Press, 2014, [2014]distribution
[Piscataqay, New Jersey] : IEEE Xplore, [2014]omfång
1 online resource (xxii, 616 pages)anmärkning
- Includes bibliographical references and index.
- Includes index.
Övriga fysiska detaljer
illustrationsAnnat bärarformat
Introduction to machine learning / · ISBN 0-262-02818-2 (Print:)kontrollnummer
6j4934th4n014kzvResursens ID / Permalänk: https://libris.kb.se/6j4934th4n014kzv#it
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