An application-focused approach to linear algebra in a variety of fields. Topics include matrices, gaussian elimination, vector spaces, determinants, inner products, orthogonality, least squares solution, eigenvalue problems, Gram-Schmidt process, matrix decomposition/factorization, Jordan canonical forms, methods of dimension reduction such as singular value decomposition and principal component analysis, quadratic forms, pseudo-inverses, Markov processes, data/image processing, and other topics pertinent to data analysis. This course is perfect for high school math and science teachers wishing to extend their knowledge of linear algebra.
Prerequisite: An undergraduate math or science degree. Additionally, a linear algebra class at the undergraduate level or some experience with the subject is preferred.
Math 527 Applied Linear Algebra (Fall 2022)
Math 546 Applied Statistics I (Winter/Spring 2022)