This is a course aiming for graduate students in science, engineering, and statistics. It covers vector spaces, linear transformations, determinants, eigenvalues and eigenvectors, canonical forms, and matrix factorizations, with applications to least squares approximations, discrete Fourier transform, differential equations, Markov chain, and image compression. The course will be accompanied by LAB assignments using Python.
Gilbert Strang, "Linear Algebra and its Applications", 4th edition, ISBN 0030105676, Brooks/Cole Publishing, 2007
Familiarity with matrices, vectors, and mathematical reasoning at the level of advanced undergraduate applied mathematics courses.