Linear Algebra in Data Science

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.

Auteur

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).