Introduction to machine learning

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of this title reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online)....

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Bibliographic Details
Main Author: Alpaydin, Ethem, (Author)
Format: eBook
Language: English
Published: Cambridge, Massachusetts : The MIT Press, [2014]
Edition: Third edition.
Series: Adaptive computation and machine learning.
Subjects:
Machine learning.
elektronické knihy
electronic books
ISBN: 9780262325745
9780262028189
Physical Description: 1 online zdroj (xxii, 613 pages) : illustrations.

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Table of contents

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100 1 |a Alpaydin, Ethem,  |e author. 
245 1 0 |a Introduction to machine learning /  |c Ethem Alpaydin. 
250 |a Third edition. 
264 1 |a Cambridge, Massachusetts :  |b The MIT Press,  |c [2014] 
264 4 |c ©2014 
300 |a 1 online zdroj (xxii, 613 pages) :  |b illustrations. 
336 |a text  |b txt  |2 rdacontent 
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338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Adaptive computation and machine learning 
520 |a Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of this title 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. --  |c Edited summary from book. 
504 |a Includes bibliographical references (page 203) and index. 
505 0 |a 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. 
590 |a IEEE  |b MIT Press eBooks Library-Computing & Engineering Collection Complete 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity 
650 0 |a Machine learning. 
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830 0 |a Adaptive computation and machine learning. 
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