Zizler Peter, La Haye Roberta. Linear Algebra in Data Science

Springer International Publishing, 2024. — 202 p. — (Compact Textbooks in Mathematics). — ISBN 978-3-031-54908-3.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. This book evolved from lecture notes for a second year university course in applications of linear algebra. We have deliberately tried to maintain that flavor in this book. We emphasize understanding over rigor and don’t espouse the Theorem and Proof style of text. We assume the reader is either familiar with foundational results in linear algebra or willing to consult a linear algebra text of their choice for specific results as they read our text. Readers with the basic linear algebra knowledge and who are interested in data science courses will find our text useful. Linear algebra is a pillar for data science, and understanding this will enable the student to grasp the procedures and techniques used. It will also provide the student with the ability to go further into the data science paradigm.Preface.
Introduction.
Projections.
Matrix Algebra.
Rotations and Quaternions.
Haar Wavelets.
Singular Value Decomposition.
Convolution.
Frequency Filtering.
Neural Networks.
Some Wavelet Transforms.
A Appendix.