Multivariate Data Analysis via Matrix Decomposition
Autumn Quarter
Instructor: Lek-Heng Lim
Syllabus
This course is about using matrix computations to infer useful information from observed data. One may view it as an "applied" version of Stat 309; 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 or "Unsupervised Learning" in Machine Learning. These include factor analysis, correspondence analysis, principal components analysis, multidimensional scaling, canonical correlation analysis, Procrustes analysis, partial least squares, etc. We would also discuss a small number of supervised learning techniques including discriminant analysis and support vector machines. Understanding these techniques requires some facility with matrices (primarily eigen and singular value decompositions, as well as their generalization) in addition to some basic statistics, both of which the student will acquire during the course.
Cross-listed with STAT 32940.