MAT2233

A comprehensive list of all undergraduate math courses at UTSA can be found here.

The Wikipedia summary of Linear Algebra and its history.

Topics List

Date	Sections from Lay	Sections from Bretscher	Topics	Prerequisite Skills	Student Learning Outcomes
Week 1	1.1 and 1.2	1.1	Introduction to Linear Systems of Equations	Adding equations and multiplying equations by constants Solving Equations and Inequalities	Using elimination to find solutions of linear systems The Geometrical interpretation of solutions to linear systems
Week 2	1.3, 1.4, and 1.5	1.2 and 1.3	Vectors and Matrices Gauss-Jordan Elimination	Introduction to Linear Systems of Equations Equation for a line	Vectors and vector addition Matrix notation The Gauss-Jordan method for solving a linear system of equation The rank of a matrix Sums of Matrices The product Ax (where A is a matrix and x is a vector) The Dot product Linear Combinations
Week 3	2.1	2.3	Matrix Algebra and Matrix Multiplication	Range of a Function Transformations of Functions	Matrix Operations Matrix products by columns Matrix products using the dot product
Week 3	2.2 and 2.3	2.4	The Inverse of a Linear Transformation	Matrix Algebra and Matrix Multiplication Inverse Functions Introduction to Linear Systems of Equations	The Identity matrix The Inverse of a Matrix Various characterizations for an invertible matrix
Week 4	1.8 and 1.9	2.1	Introduction to Linear Transformations	Introduction to Linear Systems of Equations Vectors and Matrices, Gauss-Jordan Elimination Transformations of Functions	Linear Transformations Requirements for a transformation to be linear
Week 5	1.7, 2.8, and 2.9	3.2	Subspaces of Rⁿ and Linear Independence	Matrix Algebra and Matrix Multiplication The Inverse of a Linear Transformation	Definition of a subspace of Rⁿ Defining linear independence for a set of vectors Definition of a basis for a subspace
Week 6	4.1	4.1	Introduction to Vector Spaces	Matrix Algebra and Matrix Multiplication Subspaces of Rⁿ and Linear Independence	Definition of a vector space(or linear space) Subspaces of vector spaces Linear combinations and bases for vector spaces Examples of vector spaces of functions
Week 6	4.2	3.1	The Column Space and Nullspace of a Linear Transformation	Introduction to Linear Transformations Range of a Function The Inverse of a Linear Transformation	The image (or column space) of a linear transformation The kernel (or nullspace) of a linear transformation Properties of the kernel
Week 7	4.3 and 4.5	3.3 and 4.1	The Dimension of a Vector Space	Introduction to Vector Spaces Subspaces of Rⁿ and Linear Independence	The number of vectors in a basis of Rn Dimension of a subspace in Rⁿ The dimension of a vector space The dimension of the nullspace (or kernel) and the column space (or image) The Rank-nullity Theorem
Week 8	6.1 and 6.2	Appendix A and 5.1	Dot Products and Orthogonality	The Dimension of a Vector Space Subspaces of Rⁿ and Linear Independence	Orthogonal vectors Length (or magnitude or norm) of a vector Unit vectors Orthonormal vectors Orthogonal projections Orthogonal complements Cauchy-Schwarz inequality The angle between vectors
Week 9	6.3 and 6.4	5.2 and 5.3	Orthonormal Bases and the Gram-Schmidt Process	Subspaces of Rⁿ and Linear Independence Dot Products and Orthogonality	Orthogonal transformations Orthonormal Bases Orthogonal matrices The transpose of a matrix The Gram-Schmidt Process QR factorization
Week 10	6.5 and 6.6	5.4	The Least-squares Solution	Dot Products and Orthogonality The Column Space and Nullspace of a Linear Transformation	The orthogonal complement of the image is equal to the left nullspace (or kernel of the transpose) for all matrices The least-squares solution for a linear system Data fitting using the least-squares solution
Week 11	3.1 and 3.2	6.1 and 6.2	Introduction to Determinants	Orthonormal Bases and the Gram-Schmidt Process The Inverse of a Linear Transformation Sigma Notation	The determinant of 2 by 2 and 3 by 3 matrices The determinant of a general n by n matrix The determinant of a triangular matrix Properties of the determinant The determinant of the transpose Invertibility and the determinant
Week 12	3.3	6.3	The Geometric Interpretation of the Determinant	Orthonormal Bases and the Gram-Schmidt Process Introduction to Determinants The Inverse of a Linear Transformation	Cramer's Rule The adjoint and inverse of a matrix The area of a parallelogram and the volume of a parallelepiped
Week 13	The beginning of 5.3 as well as the sections 5.1 and 5.2	7.1, 7.2 and the beginning of 7.3	Eigenvalues and Eigenvectors	Introduction to Determinants The Column Space and Nullspace of a Linear Transformation The Inverse of a Linear Transformation	The requirement for a matrix to be diagonalizable Definition of an eigenvector The characteristic equation used to find eigenvalues Eigenvalues of a triangular matrix Eigenspaces for specific eigenvalues
Week 14	5.3 and 5.4	3.4 and 7.3	Diagonalization of Matrices	Eigenvalues and Eigenvectors The Column Space and Nullspace of a Linear Transformation	Similar matrices Diagonalization in terms of linearly independent eigenvectors Algebraic and geometric multiplicity for a specific eigenvalue The strategy for diagonalization