Overview
- Explores applications of linear algebra in data science, showing readers how the two are connected
- Offers exercises that escalate in complexity, many of which incorporate MATLAB
- Includes practice projects that show real-world applications of the material covered in a standard linear algebra course
Part of the book series: Compact Textbooks in Mathematics (CTM)
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About this book
This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.
Keywords
Table of contents (10 chapters)
Authors and Affiliations
About the authors
Roberta La Haye is an associate professor of mathematics at Mount Royal University in Calgary. She holds a Ph.D. in mathematics (group theory). Her current research interests include ties between mathematics and the visual art and ties between mathematics and statistics. She has publications in mathematics journals, visual arts education journals and statistics journals as well as co-authored book chapters in Co-Teaching in Higher Education: From Theory to Co-Practice, University of Toronto Press, (2017) and Applications of the Gini Index Beyond Economics and Statistics, in the Handbook of the Mathematics of the Arts and Sciences, Springer (2021).
Bibliographic Information
Book Title: Linear Algebra in Data Science
Authors: Peter Zizler, Roberta La Haye
Series Title: Compact Textbooks in Mathematics
DOI: https://doi.org/10.1007/978-3-031-54908-3
Publisher: Birkhäuser Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Softcover ISBN: 978-3-031-54907-6Published: 15 May 2024
eBook ISBN: 978-3-031-54908-3Published: 14 May 2024
Series ISSN: 2296-4568
Series E-ISSN: 2296-455X
Edition Number: 1
Number of Pages: VIII, 199
Number of Illustrations: 14 b/w illustrations, 9 illustrations in colour
Topics: Linear Algebra, Data Structures and Information Theory, Artificial Intelligence, Mathematical Applications in Computer Science