CS 171 - Linear Algebra for Computer Science

5.0 Credits
This course uses a high-level programming language as a vehicle to discuss those aspects of linear algebra that are most important in data analytics and computer science. It will cover the theory behind linear algebra ideas as well as how and when to apply them. The main topics include basic matrix operations, linear transformations, ranges, linear combinations and spans, systems of linear equations, symmetric matrices, inverses, determinants, triangular matrices, trace, eigenvalues, and eigenvectors.
Prerequisite Completion of CS& 131 or CS& 141 with a grade of 2.5 or higher, and placement into MATH& 141, or instructor permission.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:

Solve problems using concepts and methods of linear algebra including topics such as Gauss-Jordan elimination, vector spaces, eigenvalues, eigenvectors, vector spaces, and linear transformations.
Apply linear algebra concepts to common computing, engineering, and mathematical problems.
Write a program using linear algebra to solve an appropriate data analytics problem.
Describe the relationship between computer-based activities and the application of linear algebra concepts.
Identify possible uses of linear algebra in various career fields.

Course Typically Offered
Fall