Linear Algebra: A Course for Physicists and Engineers

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

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