Advanced theory of linear algebra, including topics such as special families of matrices (symmetric, stochastic, etc.), and matrix decompositions (SVD, QR, etc.). Applications of linear algebra drawn from a variety of fields, such as linear programming, regression, ranking algorithms, principal component analysis, spectral graph theory, and Markov chains. Includes an emphasis on implementation in a programming language. Prerequisite: MATH 210 and MATH 215 and CSCI 157.