000			02552nam\a2200289\a\4500
001			57651
005			20251215141834.0
008			251215s2020 maua b 001 0 eng
010			_a 2019028373
020			_a9780262043793 _q(hardcover)
020			_z9780262358064 _q(ebook)
040			_aDLC _beng _cDLC _erda _dBD-DhIUB
082	0	0	_a006.31 _223 _bA319i
100	1		_aAlpaydin, Ethem, _eauthor.
245	1	0	_aIntroduction to machine learning / _cEthem Alpaydin.
250			_aFourth edition.
260			_aEngland: _bThe MIT Press, _c2020
300			_axxiv, 682 pages : _billustrations ; _c24 cm.
490	0		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
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"--
526			_aCSE _bps _lREF
541			_aRisaam
650		0	_aMachine learning.
942			_2ddc _cBK
999			_c57651 _d57825