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Zusatztext The book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science Informationen zum Autor Professor Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington. Prof. Kutz was awarded the B.S. in physics and mathematics from the University of Washington (Seattle, WA) in 1990 and the PhD in Applied Mathematics from Northwestern University (Evanston, IL) in 1994. He joined the Department of Applied Mathematics, University of Washington in 1998 and became Chair in 2007.Professor Kutz is especially interested in a unified approach to applied mathematics that includes modeling, computation and analysis. His area of current interest concerns phenomena in complex systems and data analysis (dimensionality reduction, compressive sensing, machine learning), neuroscience (neuro-sensory systems, networks of neurons), and the optical sciences (laser dynamics and modelocking, solitons, pattern formation in nonlinear optics). Klappentext Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies. Zusammenfassung Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies. Inhaltsverzeichnis I Basic Computations and Visualization 1: MATLAB Introduction 2: Linear Systems 3: Curve Fitting 4: Numerical Differentiation and Integration 5: Basic Optimization 6: Visualization II Differential and Partial Differential Equations 7: Initial and Boundary Value Problems of Differential Equations144 8: Finite Difference Methods 9: Time and Space Stepping Schemes: Method of Lines 10: Spectral Methods 11: Finite Element Methods III Computational Methods for Data Analysis 12: Statistical Methods and Their Applications 13: Time-Frequency Analysis: Fourier Transforms and Wavelets 14: Image Processing and Analysis 15: Linear Algebra and Singular Value Decomposition 16: Independent Component Analysis 17: Image Recognition 18: Basics of Compressed Sensing 19: Dimensionality Reduction for Partial Differential Equations 20: Dynamic Mode Decomposition 21: Data Assimilation Methods 22: Equation Free Modeling IV Scientific Applications 23: Applications of Differential Equations and Boundary Value Problems 24: Quantum Mechanics 25: Applications of Partial Differential Equations 26: Applications of Data Analysis ...
