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Course Syllabus

MATH 2270 Linear Algebra

  • Division: Natural Science and Math
  • Department: Mathematics
  • Credit/Time Requirement: Credit: 3; Lecture: 3; Lab: 0
  • Prerequisites: MATH 1210
  • Semesters Offered: Fall, Spring
  • Semester Approved: Spring 2023
  • Five-Year Review Semester: Fall 2027
  • End Semester: Fall 2028
  • Optimum Class Size: 24
  • Maximum Class Size: 30

Course Description

Linear algebra is a study of systems of linear equations, matrices, vectors and vector spaces, linear transformations, eigenvalues and eigenvectors, and inner product spaces. This class is required for students majoring in mathematics and many areas of science and engineering.

Justification

This class is required for students majoring in mathematics, computer science, as well as many branches of engineering, physics, and chemistry. The material from this course provides a foundation and problem-solving tools for students entering science-related fields. Linear Algebra is also recommended in some allied fields. Linear algebra concepts are common in advanced math and science contexts. This course is fully transferable to all Utah public higher education institutions (Utah State University, University of Utah, Weber State University, Southern Utah University, Dixie State University, Utah Valley University, Salt Lake Community College) as well as Brigham Young University.

Student Learning Outcomes

  1. Students will know and be able to use principles of matrix operations.
  2. Students will understand and be able to perform desired operations on vectors in 2-, 3-, and n-dimensions.
  3. Students will understand and apply inner products to vector spaces.
  4. Students will compute eigenvalues and eigenvectors (by hand and with technology, asappropriate).

Course Content

Through class discussion, class activities, lecture, and practice in homework and/or projects, students will learn the concepts below. * Systems of linear equations and their solutions * Matrix arithmetic * Determinants* Vector arithmetic * Vector spaces and linear transformations * Inner product spaces* Gram-Schmidt Process* Least squares approximation * Eigenvalues and eigenvectors, including matrix diagonalizationInstructors are encouraged to foster an environment where different correct approaches to problems are considered and appreciated.