http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
data science, algorithms, and computational statistics
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.
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.
Includes bibliographical references and index.
Machine Learning for Signal Processing : Data Science, Algorithms, and Computational Statistics
Oxford : Oxford University Press USA - OSO, ©2019