Название: Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models, 3rd EditionАвтор: Sergios Theodoridis
Издательство: Academic Press/Elsevier
Год: 2026
Страниц: 1220
Язык: английский
Формат: pdf (true)
Размер: 21.4 MB
Machine 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. 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. Most chapters include a number of computer exercises in both MatLab and Python, and the chapters dedicated to Deep Learning include exercises in PyTorch.
