Applied Linear Algebra ((4 Credits))

Catalog description

This course introduces students to the concepts and methods of linear algebra with an emphasis on applications in computer science, engineering, and data science. Topics include systems of linear equations, matrix algebra, determinants, vector spaces, eigenvalues and eigenvectors, and orthogonality. Students will learn to apply linear algebraic methods to real-world problems such as image compression, network analysis, population modeling, and optimization. Emphasis will be placed on computational techniques enabling students to implement algorithms and explore applied examples in data and technology domains. This course serves as a foundation for advanced work in applied mathematics, machine learning, and quantitative modeling. Required: CSCI 2020 – Introduction to Programming with Python, or equivalent course in Python programming. Recommended: MATH 2140 – Calculus I (may be taken concurrently).