CS 6241 - Numerical Methods for Data Science

Spring. 3 credits. Student option grading.

Prerequiste: Strong background in linear algebra, prior exposure to numerical methods.

D. Bindel.

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.