Linear Algebra with Applications

Focuses on vectors, matrices, and least squares with real-world applications including clustering, data fitting, and classification. Used at Stanford and other top universities.

Stephen Boyd, Lieven Vandenberghe

Written for non-mathematicians who need linear algebra for physics and engineering. Covers vector spaces, matrices, and applications with accessible explanations and practical examples.

Arak M. Mathai, Hans J. Haubold

A comprehensive treatment of linear algebra with applications to computer vision, robotics, and machine learning. Covers SVD, spectral theory, Haar wavelets, and optimization methods.

Jean Gallier, Jocelyn Quaintance