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Data-Driven Science and Engineering : Machine Learning, Dynamical Systems, and Control

By: Steven L. Brunton J. Nathan Kutz (Author)

Ksh 9,000.00
Format :

Format(s): DRM PDF

ISBN-13: 9781009115636

Publisher: Cambridge University Press

Imprint: Cambridge University Press

Publication Date: April 27th, 2022

Country of Publication: GB

Language: English

Physical Edition EAN: 9781009098489

Category: Probability & statistics , Optimization , Mathematical modelling , Mathematical physics , Automatic control engineering , Mathematical theory of computation , Machine learning , Signal processing

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  • Description

  • Author Bio

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB(R), this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material - including lecture videos per section, homeworks, data, and code in MATLAB(R), Python, Julia, and R - available on databookuw.com.

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