MATH412

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MATH412 - Linear Algebra for Data Science

Mathematics Undergraduate UA - UA General

Course Description

This course will cover some of the more advanced topics in Linear Algebra beyond what is covered in traditional undergraduate courses. The focus will be on exploring theory which is used in real Data Science applications, including matrix factorization, low-rank matrix approximations, kernel methods, graph theory, and optimization. Some special modern topics will be covered such as Compressed Sensing, Data Clustering, and Frame Theory. The theory will be complemented with illustrative applications.

Min Units

3

Max Units

3

Repeatable for Credit

No

Grading Basis

GRD - Regular Grades A, B, C, D, E

Career

Undergraduate

Course Attributes

CE - CL (Cross Listed)

Enrollment Requirements

019025

Cross Listed Courses

Name

Lecture

Workload Hours

3

Optional Component

No