Brunton, Steven L.  1984- <br/><br/>
Data-driven science and engineering :  machine learning, dynamical systems, and control / 
Steven L. Brunton and J. Nathan Kutz. 
 - xxii, 472 pages :   illustrations ;   26 cm.  
<br/><br/>Includes bibliographical references and index. 
<br/><br/>Singular value decomposition (SVD) -- Fourier and wavelet transforms -- Sparsity and compressed sensing -- Regression and model selection -- Clustering and classification -- Neural networks and deep learning --  Data - driven dynamical systems -- Linear control theory -- Balanced models for control -- Data - driven control -- Reduced order models (ROMs) -- Interpolation for parametric ROMs. 
<br/><br/> "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art"-- 
<br/><br/><label>ISBN: </label>9781108422093 
<br/><br/><label>LCCN: </label>2018029888 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Engineering--Data processing.<br/>Science--Data processing.<br/>Mathematical analysis.
<br/><br/><label>LC Class. No.: </label>TA330  / .B78 2019 
<br/><br/><label>Dewey Class. No.: </label>620.00285631  / B838d