- Schedule of Classes - June 18, 2018 7:14PM EDT
- Course Catalog - June 14, 2018 7:15PM EDT
Course information provided by the Courses of Study 2017-2018.
A discussion of numerical methods (particularly iterative methods for linear algebra and optimization) in the context of machine learning and data analysis problems. The course will particularly focus on sparsity, rank structure, and spectral behavior of underlying linear algebra problems; convergence behavior and "regularization via iteration" effects for standard solvers; and comparisons between numerical methods for data analysis with large-scale numerical methods used in other areas of science and engineering.
When Offered Spring.
Prerequisites/Corequisites Prerequiste: Strong background in linear algebra, prior exposure to numerical methods.
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- TRHollister Hall B14
Limited to grad students only.
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