Linear Algebra with Computational Applications

Description

This is a first course in linear algebra. This covers basic definitions and algorithms of the subject needed in the higher level (engineering, science, and economics) courses and more sophisticated mathematical techniques such as the Singular Value Decomposition.

In this course, you learn the mathematical theory and how to implement it in Python. You will discover many of the striking modern applications of linear algebra.

The course covers the same mathematical theory as MATH 415, but adds a focus on the computational and large data aspect of linear algebra through the lab sessions.

Student Learning Outcomes

Upon successful completion of this course, students will be able to:

• Demonstrate an understanding of matrices and gaussian elimination, vector spaces, orthogonality, determinants, eigenvalues and eigenvectors, and positive definite matrices.

• Apply numerical, computational, and estimation techniques.

• Use matrices to model and analyze physical phenomena.

• Explain and use the tools to formulate and solve problems in mathematical situations and connect concepts covered to other disciplines.

• Communicate ideas through descriptive language, as well as through mathematical symbols.