MATH 2170 - Applied Linear Algebra

3 Credit(s) | $3.75 Fee

An introduction to linear algebra, with emphasis on data science and machine learning. Topics include vectors, inner products, norms, linear independence, orthonormal sets, Gram-Schmidt algorithm, clustering and the k-means algorithm, linear systems, matrix algebra, matrix inverses, linear and affine transformations, linear dynamical systems, least-squares, data fitting, eigenvalues, and singular value decomposition. Additional applications may include QR factorization, adjacency matrices and network flow, computer graphics, regularization, cross-validation, classification, constrained least-squares, time-series prediction, linear quadratic control, dimensionality reduction, principal component analysis, and portfolio optimization. Students will use Python throughout this course. (Fall, Spring) [Graded Letter]

Prerequisite(s): (MATH 1100 or MATH 1210) and (ANLY 2500 or CSCY 1300 or CS 1400 or CS 1410 or instructor approval) - Prerequisite Min. Grade: C
Prerequisite Can Be Concurrent? Yes (all courses)