MTH 338 - Applied Linear Algebra for Data Science

This course builds on foundational elements of linear algebra by applying these ideas to important topics in the field of Data Science. Students will use matrices and vectors to represent multidimensional data models and explore methods of choosing optimal parameters to fit provided data. Software will make these vectorized approaches concrete. Further topics may include important matrix decompositions, such as Principal Component Analysis and Singular Value Decomposition, as well as their applications.

Prerequisites & Notes
MTH238

(Cr: 3)