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 require 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.
Location: Lectures held online through Canvas.
Times: Mon, 6:30–9:30pm
Office: Jones 122C
Tel: (773) 702-4263
Office hours: Tue 2:00–4:00 pm
Course Assistant I: Jinhong Du
Office hours: Fri 3:00–5:00 pm.
Course Assistant II: Wenxuan
Office hours: Tue 9:00–11:00 am.
The last two applications fall under supervised learning but we will discuss them if time permits, if only to give an idea of how supervised learning differs from unsupervised learning.
Collaborations are permitted but you will need to write up your own solutions and declare your collaborators. The problem sets are designed to get progressively more difficult. You will get about 10 days for each problem set.
You are required to implement your own programs for problems that require some amount of simple coding (using Matlab, Mathematica, R, or SciPy).
Bug report on the problem sets: lekheng(at)uchicago.edu
Grade composition: 100% Problem Sets.
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