Mathematical methods in data science
- Amsterdam : Elsevier, 2023
- xi, 246 p. ; ill. (some col.), 23 cm.
Includes bibliographical references and index.
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors' recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and a clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.
9780443186790
Mathematical models Differential Equations Partial differential equations Lyapunov exponent Linear subspace QR decomposition Laplacian matrix; PDE model Influenza Gradient descent Eigenvectors Chainlet clusters