000			03747nam a2200517 i 4500
001			6267367
003			IEEE
005			20220712204643.0
006			m o d
007			cr |n|||||||||
008			151223s2009 mau ob 001 eng d
020			_a9789533070346
020			_a9780262267052 _qelectronic
020			_z9780262012430 _qhardcover
035			_a(CaBNVSL)mat06267367
035			_a(IDAMS)0b000064818b4371
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.A46 2010eb
082	0	0	_a006.3/1 _222
100	1		_aEthem Alpaydd n., _eauthor. _922380
245	1	0	_aIntroduction to machine learning / _cEthem Alpaydd n.
250			_a2nd edition.
264		1	_aCambridge, Massachusetts : _bMIT Press, _c[2010]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2009]
300			_a1 PDF (xl, 539 pages.).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThe 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. The second edition of 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. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
546			_aen
588			_aTitle from title screen.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _922381
710	2		_aMIT Press, _epublisher. _922382
776	0	8	_iPrint version _z9780262012430
830		0	_aAdaptive computation and machine learning. _921570
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267367
942			_cEBK
999			_c73022 _d73022