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Data Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2/Ed > 데이터마이닝

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Data Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2/Ed
히트도서
판매가격 85,000원
도서종류 외국도서
출판사 Cambridge University Press
발행언어 영어
발행일 2022
페이지수 614
ISBN 9781009098489
배송비결제 주문시 결제
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    도서 상세설명

    About the Author

    Steven L. Brunton is the James B. Morrison Professor of Mechanical Engineering at the University of Washington and Associate Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Applied Mathematics and Computer Science and a Data-Science Fellow at the eScience Institute. His research merges data science and machine learning with dynamical systems and control, with applications in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is an author of three textbooks, and received the UW College of Engineering Teaching award, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE) award.

    J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington and Director of the NSF AI Institute in Dynamic Systems. He is also Adjunct Professor of Electrical and Computer Engineering, Mechanical Engineering, and Physics and Senior Data-Science Fellow at the eScience Institute. His research interests lie at the intersection of dynamical systems and machine learning. He is an author of three textbooks and has received the Applied Mathematics Boeing Award of Excellence in Teaching and an NSF CAREER award.

    Table of Contents

    Part I. Dimensionality Reduction and Transforms: 1. Singular Value Decomposition; 2. Fourier and Wavelet Transforms; 3. Sparsity and Compressed Sensing;

    Part II. Machine Learning and Data Analysis: 4. Regression and Model Selection; 5. Clustering and Classification; 6. Neural Networks and Deep Learning; 

    Part III. Dynamics and Control: 7. Data-Driven Dynamical Systems; 8. Linear Control Theory; 9. Balanced Models for Control; 

    Part IV. Advanced Data-Driven Modeling and Control: 10. Data-Driven Control; 11. Reinforcement Learning; 12. Reduced Order Models (ROMs); 13. Interpolation for Parametric ROMs; 14. Physics-Informed Machine Learning.

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