Dec 05, 2025  
2025-2026 Catalog SVC 
    
2025-2026 Catalog SVC

MATH 204 - Elementary Linear Algebra


Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: Introduction to linear algebra covering systems of linear equations, matrices, vector spaces and subspaces, spanning sets, eigenvalues and eigenvectors, transformations, determinants and applications. Graphing Technology required.

Prerequisite: MATH& 151 with a C or higher.
Meets FQE Requirement: No
Elective Requirements: Fulfills Academic Electives
Integrative Experience Requirement: No

Student Learning Outcomes
  1. Solve systems of equations using Gauss-Jordan elimination.
  2. Reduce a matrix to row-reduced echelon form using row reduction.
  3. Test for linear independence in Rn.
  4. Reduce a spanning set to a basis in Rn.
  5. Compute the row space, column space, and null space of a matrix.
  6. Compute a basis for the kernel and image of a linear transformation.
  7. Factor a matrix using LU factorization and by diagonalization.
  8. Compute the eigenvalues and eigenvectors of a matrix.

Course Contents
  1. Linear Systems of Equations and Matrices. Gauss Jordan row reduction. Vector equations. Matrices and matrix operations. Inverse of a matrix. Elementary matrices and the inverse matrix. Matrix of a linear transformation. Characterizations of invertible matrices. Linear independence in Rn. LU factorization of a matrix. 
  2. Determinants. Determinants by co-factor expansion. Calculating determinants by row reduction. Properties of determinants. 
  3. General Vector Spaces. Vector spaces and subspaces. Linear independence. Coordinate systems. Change of basis. Dimensions of a vector space. Rank and nullity. Matrix transformations from Rn to Rm.
  4. Eigensystems. Eigenvalues and eigenvectors. The characteristic equation. Diagonalization. Complex eigenvalues. 
  5. Optional Applications. Cramer’s Rule. Markov chains. Leontief Input-Output model. Applications to computer graphics. Kirchhoff’s Law. Difference equations. Networks. Orthogonality and least squares. Curve fitting. Linear programming.


Instructional Units: 5