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100	1		_aAlpaydin, Ethem _9165255
245	1	0	_aIntroduction to machine learning / _cEthem Alpaydin.
250			_a4ª edición.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c2020.
300			_a683 páginas : _bfiguras ; _c24 centímetros.
336			_2rdacontent _atexto _btxt
337			_2rdamedia _asin mediación _bn
338			_2rdacarrier _avolumen _bnc
490	0		_aAdaptive computation and machine learning series
500			_aIncluye referencias bibliográficas e índice.
520			_aThe 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.
650		0	_aAprendizaje automático _9165256
650		0	_aInteligencia artificial
650		0	_aMinería de datos _944500
650		0	_aRedes neuronales (informática) _9165257
650		0	_aAprendizaje supervisado _9165258
700	1		_aAlpaydin, Ethem. _9165255
942			_2ddc _cBK
999			_c48627 _d48627