Machine Learning: A Bayesian and Optimization Perspective, 2nd edition

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified 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. It then progresses to more recent techniques, covering sparse modelling 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 modelling are also considered in depth.

This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of 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, all the various methods and techniques are explained in depth, supported by examples and problems, serving as a resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises in MATLAB. In addition, MATLAB code is available on an accompanying website, enabling the reader to experiment with the code.

About This Book

Sergio Theodoridis, National and Kapodistrian University of Athens & Chinese University of Hong Kong

Academic Press, 2020

ISBN: 9780128188033
Language: English

Buy Now at Amazon.com

Online Teaching with MATLAB and Simulink

Whether you are transitioning a classroom course to a hybrid model, developing virtual labs, or launching a fully online program, MathWorks can help you foster active learning no matter where it takes place.

MATLAB Courseware

Teaching materials based on MATLAB and Simulink.

Find full courses and labs