MATH-308 Applied Linear Algebra
Fall for 2017-2018
J K Shaw
Description. This is a course which covers contemporary mathematical and statistical applications that involve data and use discrete or linear algebra methods for analysis. The topics include data compactification such as image compression, principal components analysis (eg, dimension reduction), Google page rank algorithm, couples counseling (from Journal of Family Psychology), Markov chains (eg, genetic mutations), approximate solutions of ordinary and partial differential equations, ODE systems such as predator-prey, singular value decomposition (eg, more data reduction), dimensional analysis via Gaussian elimination, competing search engines, discrete predator-prey models and discrete economic models. The software Matlab will be used for non-trivial matrix operations; instruction in Matlab will be provided and students will need to download Matlab (free) via the university site license. The instructor plans to use the Zoom lecture capture technology provided that an adequate classroom is assigned; students should download Zoom from Georgetown’s UIS website. No textbook is required; class will use desktop-published notes by the instructor.

Prerequisites: Math 150 Linear Algebra or equivalent
Credits: 3
Prerequisites: Math 150 Linear Algebra or equivalent.
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