000			04462nam a2200637 i 4500
003			KOHA
005			20231016174104.0
007			KOHA
008			220514s2020 maud 001 0 eng d
020			_a9780262043793 _q(hardback)
040			_aTR-IsMEF _beng _cOZU _dT9K _dTR-IsMEF _erda
041	0		_aeng
050	0	0	_aQ325.5 _b.A46 2020
100	1		_aAlpaydın, Ethem, _eauthor.
245	1	0	_aIntroduction to machine learning / _cEthem Alpaydın
250			_aFourth edition.
264		1	_aCambridge, Massaschusetts : _bThe MIT Press, _c2020.
300			_axxiv, 682 pages : _bcharts ; _c24 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	1		_aAdaptive Computation and Machine Learning Series.
500			_aIncludes index (pages 673-682).
520	0		_aAvailable at a lower price from other sellers that may not offer free Prime shipping. A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.--backover. _uhttps://www.amazon.com/Introduction-Machine-Learning-Adaptive-Computation/dp/0262043793
650		7	_aMachine learning
650		7	_aArtificial intelligence
650		0	_aLinear discrimination
830		0	_tAdaptive Computation and Machine Learning Series. _942304
900			_aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules
910			_aPandora
942			_2lcc _cBKS _01
970	1	2	_l1 _tIntroduction, _p1.
970	1	2	_l2 _tSupervised Learning, _p23.
970	1	2	_l3 _tBayesian Decision Theory, _p51.
970	1	2	_l4 _tParametric Methods, _p67.
970	1	2	_l5 _tMultivariate Methods, _p95.
970	1	2	_l6 _tDimensionality Reduction, _p117.
970	1	2	_l7 _tClustering, _p165.
970	1	2	_l8 _tNonparametric Methods, _p189.
970	1	2	_l9 _tDecision Trees, _p217.
970	1	2	_l10 _tLinear Discrimination, _p243.
970	1	2	_l11 _tMultilayer Perceptrons, _p271.
970	1	2	_l12 _tDeep Learning, _p313.
970	1	2	_l13 _tLocal Models, _p361.
970	1	2	_l14 _tKernel Machines, _p395.
970	1	2	_l15 _tGraphical Models, _p433.
970	1	2	_l16 _tHidden Markov Models, _p463.
970	1	2	_l17 _tBayesian Estimation, _p491.
970	1	2	_l18 _tCombining Multiple Learners, _p533.
970	1	2	_l19 _tReinforcement Learning, _p563.
970	1	2	_l20 _tDesign and Analysis of Machine Learning Experiments, _p597.
970	1	1	_lA _tProbability, _p643.
970	1	1	_lB _tLinear Algebra, _p655.
970	1	1	_lC _tOptimization, _p665.
999			_c26499 _d26499