Semester:
N/A
Offered:
2015
Instructor: Eli Tziperman
Description: Topics in linear algebra which arise frequently in applications, including in the analysis of large data sets: linear equations, eigenvalue problems, principal component analysis, singular value decomposition, quadratic forms, linear inequalities, linear programming, optimization, linear differential equations, modeling and prediction, data mining methods including frequent pattern analysis, classification, clustering, outlier detection.
Credit: Half Course (Spring term)
Prerequisites: Applied Mathematics 21a and 21b, or equivalent.