MATH 2170 - Applied Linear Algebra

3 Credit(s)

Introduction to applied linear algebra with emphasis on applications. Vectors, inner products, linear independence, orthonormal sets, Gram-Schmidt algorithm, applications to document analysis. Clustering and the k-means algorithm. Matrix algebra, left and right inverses, QR factorization, linear dynamical systems. Least-squares and data fitting. Additional applications may include regularization, cross-validation, constrained least-squares, time-series prediction, and portfolio optimization. Students will use Python throughout this course. (Fall) [Graded (Standard Letter)]

Prerequisite(s): (MATH 1100 or MATH 1210 or ECON 2500) and (CSIS 1030 or CSIS 1300 or CS 1040 or CS 1400 or CS 1410 or instructor approval) - Prerequisite Min. Grade: C
Registration Restriction(s): None