000			02997cam a22002895i 4500
003			EG-CaNGU
005			20240131123917.0
008			231121t2020 maua frb 001 0 eng d
020			_a9780262043793 _q(hardcover)
040			_aDLC _beng _cDLC _erda _dDLC _dEG-CaNGU
082	0	4	_a006.31 _bALI _223
100	1		_aAlpaydin, Ethem, _eauthor. _95913
245	1	0	_aIntroduction to machine learning /
250			_aFourth edition.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2020]
264		4	_c©2020
300			_axxiv, 682 pages : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
505	0	0	_aIntroduction -- 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 -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
520			_a"Since the third edition of this text appeared in 2014, most recent advances in machine learning, both in theory and application, are related to neural networks and deep learning. In this new edition, the author has extended the discussion of multilayer perceptrons. He has also added a new chapter on deep learning including training deep neural networks, regularizing them so they learn better, structuring them to improve learning, e.g., through convolutional layers, and their recurrent extensions with short-term memory necessary for learning sequences. There is a new section on generative adversarial networks that have found an impressive array of applications in recent years. Alpaydin has also extended the chapter on reinforcement learning to discuss the use of deep networks in reinforcement learning. There is a new section on the policy gradient method that has been used frequently in recent years with neural networks, and two additional sections on two examples of deep reinforcement learning, which both made headlines when they were announced in 2015 and 2016 respectively. One is a network that learns to play arcade video games, and the other one learns to play Go. There are also revisions in other chapters reflecting new approaches, such as embedding methods for dimensionality reduction, and multi-label classification. In response to requests from instructors, this new edition contains two new appendices on linear algebra and optimization, to remind the reader of the basics of those topics that find use in machine learning"-- _cProvided by publisher.
650		7	_aMachine learning. _2NGU-sh _93897
999			_c1970 _d1970