Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis.

Previous edition: London: Academic Press, 2015.

Includes bibliographical references and index.

Introduction -- Probability and stochastic processes -- Learning in parametric modeling : basic concepts and directions -- Mean-square error linear estimation -- Online learning : the stochastic gradient descent family of algorithms -- The least squares family -- Classification : a tour of the classics -- Parametric learning : a convex analytic path -- Sparsity-aware learning : concepts and theoretical foundations -- Sparsity-aware learning : algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning : inference and the EM algorithm -- Bayesian learning : approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models : part I -- Probabilistic graphical models : part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction and latent variable modeling.

"Machine learning: A Bayesian and optimization perspective, 2nd edition, gives a unifying perspective on machine learning by covering both pillars of supervised learning, namely, regression and classification. The book starts with the basics, including mean-square, least-squares, and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. Then it moves on to more recent techniques, with emphasis on sparse modeling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models, and particle filtering. Dimensionality reduction and latent variables modeling are also considered in depth. The palette of techniques is concluded with an extended chapter on neural networks and deep learning architectures. The book also pays tribute to and covers fundamentals on statistical parameter estimation, Wiener and Kalman filtering, convexity, and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts..." -- from back cover.