Course: STAT 32940=CAAM 32940, FINM 41901
Title: Multivariate Data Analysis via Matrix Decompositions
Instructor(s): Lek-Heng Lim
Teaching Assistant(s): Gregory Naisat
Class Schedule: Sec 01: M 6:00 PM–8:50 PM in Kent 120
Description: This course is about using matrix computations to infer useful information from observed data. One may view it as an "applied" version of Stat 30900 although it is not necessary to have taken Stat 30900; the only prerequisite for this course is basic linear algebra. The data analytic tools that we will study will go beyond linear and multiple regression and often fall under the heading of "Multivariate Analysis" in Statistics. These include factor analysis, correspondence analysis, principal components analysis, multidimensional scaling, linear discriminant analysis, canonical correlation analysis, cluster analysis, etc. Understanding these techniques require some facility with matrices in addition to some basic statistics, both of which the student will acquire during the course.