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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are ...
This survey of the most important properties of the Chebyshev polynomials encompasses several areas of mathematical analysis: interpolation theory, orthogonal polynomials, approximation theory, numerical integration ...
This original work discusses mathematical methods needed by undergraduates in the United States and Canada preparing for competitions at the level of the International Mathematical ...
An outstanding introduction to tensor analysis for physics and engineering students, this text admirably covers the expected topics in a careful step-by-step manor. In addition ...
"An elegant piece of work, suitable as a text for the beginning student as well as pleasant and informative reading for the mature mathematician." - Scripta ...
This concise book on the functions of a complex variable provides the basics on a number of important topics: . Conformal representation . Special transformations . The complex ...
Requiring only an elementary knowledge of ordinary differential equations, this concise text is dedicated to helping engineering students solve problems in their field involving partial ...
This enthusiastically reviewed text, which assumes one year of high school algebra and one of plane geometry, provides a unified treatment of algebra, analytic geometry ...
Nonnegative matrices is an increasingly important subject in economics, control theory, numerical analysis, Markov chains, and other areas. This concise treatment is directed toward undergraduates ...
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics ...
