Mathematical foundations -- Optimization -- Random sampling -- Statistical modelling and inference -- Probabilistic graphical models -- Statistical machine learning -- Linear-Gaussian systems and signal processing -- Discrete signals : sampling, quantization and coding -- Nonlinear and non-Gaussian signal processing -- Nonparametric Bayesian machine learning and signal processing
Summary
Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications
Bibliography
Includes bibliographical references and index
Notes
Online resource; title from digital title page (viewed on November 07, 2019)
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