Sergios Theodoridis ... 1062 pages - Publisher: Academic Press; 1st edition (April, 2015) ... Language: English - ISBN-10: 0128015225 - ISBN-13: 978-0128015223 ...
This tutorial text gives a unifying
perspective on machine learning by covering both probabilistic and
deterministic approaches -which are based on optimization techniques –
together with the Bayesian inference approach, whose essence lies in the
use of a hierarchy of probabilistic models. The book presents the
major machine learning methods as they have been developed in different
disciplines, such as statistics, statistical and adaptive signal
processing and computer science. Focusing on the physical reasoning
behind the mathematics, all the various 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.The book builds carefully
from the basic classical methods to the most recent trends, with
chapters written to be as self-contained as possible, making the text
suitable for different courses: pattern recognition,
statistical/adaptive signal processing, statistical/Bayesian learning,
as well as short courses on sparse modeling, deep learning, and
probabilistic graphical models. All major classical
techniques: Mean/Least-Squares regression and filtering, Kalman
filtering, stochastic approximation and online learning, Bayesian
classification, decision trees, logistic regression and boosting
methods. * The latest trends: Sparsity, convex analysis and
optimization, online distributed algorithms, learning in RKH spaces,
Bayesian inference, graphical and hidden Markov models, particle
filtering, deep learning, dictionary learning and latent variables
modeling. * Case studies - protein folding prediction, optical
character recognition, text authorship identification, fMRI data
analysis, change point detection, hyperspectral image unmixing, target
localization, channel equalization and echo cancellation, show how the
theory can be applied. * MATLAB code for all the main algorithms
are available on an accompanying website, enabling the reader to
experiment with the code.
