Linear Algebra for Data Science, Machine Learning, and Signal Processing

Discover the essential techniques of linear algebra with "Linear Algebra for Data Science, Machine Learning, and Signal Processing" by Jeffrey A. Fessler, published by Cambridge University Press in 2024. This hardback edition, spanning 450 pages, provides a comprehensive exploration of basic matrix methods and their practical applications in data-driven fields.

Designed for upper-level undergraduates and first-year graduate students, this book features a variety of engaging exercises that facilitate quizzes, self-study, and interactive learning. Additionally, it includes online JULIA demos, offering a hands-on experience that enhances understanding and retention of key concepts. Dive into the world of mathematics as it applies to modern technology and equip yourself with the skills necessary for success in data science and machine learning.