Linear Algebra in Data Science

2024
ISBN: 978-3-031-54907-6
Verlag: Springer International Publishing

Buch, Englisch, 199 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 324 g

Reihe: Compact Textbooks in Mathematics

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

La Haye, Roberta

Zizler, Peter

Fachgebiete

• Mathematik | Informatik Mathematik Algebra Lineare und multilineare Algebra, Matrizentheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

Über Autor(innen)

Peter Zizler is a professor of mathematics at Mount Royal University in Calgary. He holds a Ph.D. in mathematics (linear algebra). He has publications in various journals with research done in diverse areas of linear algebra, Fourier analysis and wavelet analysis. He has also published articles on the Gini index and Lorenz curve, research papers in mathematical education and statistics. He has also previously co-authored a textbook with Springer, Introduction to Modern Analysis (2015), as well as  a chapter Applications of the Gini Index Beyond Economics and Statistics, in the Handbook of the Mathematics of the Arts and Sciences, Springer (2021).  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).