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Linear Algebra for 21st Century Applications

Linear Algebra for 21st Century Applications

Publisher Oxford University Press
Year 01/06/2020
Edition First
Pages 688
Version paperback
Readership level College/higher education
Language English
ISBN 9780198856405
Categories Mathematical foundations, Algebra, Applied mathematics
$55.61 (with VAT)
209.00 PLN / €46.71 / £41.62
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Book description

Customarily, much of traditional mathematics curricula was predicated on 'by hand' calculation. However, ubiquitous computing requires us to refresh what we teach and how it is taught. This is especially true in the rapidly broadening fields of Data Mining and Artificial Intelligence, and also in fields such as Bioinformatics, which all require the use of Singular Value Decomposition (SVD). Indeed SVD is sometimes called the jewel in the crown of linear algebra. Linear Algebra for 21st Century Applications adapts linear algebra to best suit modern teaching and application, and it places the SVD as central to the text early on to empower science and engineering students to learn and use potent practical and theoretical techniques. No rigour is lost in this new route as the text demonstrates that most theory is better proved with an SVD. In addition to this, there is earlier introduction, development, and emphasis on orthogonality that is vital in so many applied disciplines throughout science, engineering, computing and increasingly within the social sciences. A strong graphical emphasis takes advantage of the power of visualisation in the human brain and examples are included to exhibit modern applications of linear algebra, such as GPS, text mining, and image processing. Active learning is encouraged with exercises throughout that are aimed to enhance lectures, quizzes, or 'flipped' teaching.

Linear Algebra for 21st Century Applications

Table of contents

1: Vectors
2: Systems of linear equations
3: Matrices encode system interactions
4: Eigenvalues and eigenvectors of symmetric matrices
5: Approximate matrices
6: Determinants distinguish matrices
7: Eigenvalues and eigenvectors in general

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