Mar 30, 2023
MATH 220 - Linear Algebra5 Credits
Introduction to Linear Algebra: Row operation, matrix algebra; vector spaces, orthogonality, Gram-Schmidt orthogonalization, projections, linear transformations and their matrix representations, rank, similarity; determinants; eigenvalues, eigenvectors, and least squares.
Pre-requisite(s) MATH& 152 min 2.0
Course Note Math& 153 recommended.
Quarters Typically Offered
Designed to Serve Transfer students in mathematics, engineering, and the sciences
Active Date 2014-04-25
Grading Basis Decimal Grade
Class Limit 32
Contact Hours: Lecture 55 Lab 0 Field Studies 0 Clinical 0 Independent Studies 0
Total Contact Hours 55
- Quantitative Skills
ProfTech Related Instruction
PLA Eligible Yes
- Gaussian elimination, matrix algebra, elementary matrices.
- Vector spaces, geometric vectors, subspaces, bases, linear independence/dependence, dimension.
- Vector product spaces, orthogonality, Gram-Schmidt process, orthogonal projections.
- Linear transformations and their matrix representations, change of basis, similarity.
- Properties of determinants, eigenvalues, eigenvectors, eigenspaces.
- Applications; data fitting, least squares.
- Emphasis: Mathematical proof (reading, understanding, and writing).
Student Learning Outcomes
Solve systems using Gauss-Jordan elimination.
Identify and orthogonalize the basis of a vector space.
Apply matrix methods to model a data set using least squares regression.
Calculate and interpret the eigenvalues and eigenvectors of a matrix.
Identify, create, and apply linear transformations using matrix methods.
Construct a mathematical proof.
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