Linear Algebra for Computer Vision, Robotics, and Machine Learning

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

Balances theory with practical applications across linear equations, matrix algebra, determinants, vector spaces, eigenvalues, orthogonality, and singular value decomposition.

W. Keith Nicholson

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