000			01902nam a22002297a 4500
005			20260804153202.0
008			260804s2026 |||||||| |||| 00| 0 eng d
020			_a9780443292385
040			_cPK-LaUMT
082			_a006.31 _bTHE-M
100	1		_aTheodoridis, Sergios _92405
245	1	0	_aMachine learning : _bfrom the classics to deep networks, transformers, and diffusion models / _cSergios Theodoridis
250			_a3rd ed.
260			_aLondon : _bAcademic Press, _c2026
300			_axxx, 1190 p.
500			_aIncludes bibliographical references and index.
520			_aMachine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs.
546			_aEng
650			_aMachine learning
650			_aOnline learning _96501
942			_cBK
999			_c141894 _d141894