DATA 374 - Computational Linear Algebra

Credits: 3
Credit Configuration: 2+2, 3 Cr.

A survey of computational applications of linear algebra for solving science and engineering problems. Topics include linear systems in both equation and matrix form, row operations, inverses and determinants, eigenvalues, Gaussian elimination, matrix decomposition (including singular value and QR decompositions), sparse vs. dense matrices, and approximation. Topics are explored through applications to large data sets, with programming in both Python and MATLAB environments. Other advanced topics related to data mining may be covered.

Prerequisite(s): one of CS 157 or DATA 151, and one of MATH 260 or MATH 264.