Math 22
Linear Algebra with Applications

Last updated July 11, 2017 09:35:00 EDT

## Syllabus

The following is a tentative syllabus for the course. This page will be updated irregularly.
On the other hand, the weekly syllabus contained in the Homework Assignments page will always be accurate.

Lectures Sections in Text Brief Description
3/27 1.1 Systems of Linear Equations
3/29 1.2 Row Reduction and Echelon Forms
3/31 1.3, 1.4 Vector Equations; Matrix Equations
4/3 1.4, 1.5 Matrix Equations; Solutions Sets of Linear Equations
4/5 1.7 Linear Independence
4/7 4.1, 4.2 Vector spaces, linear transformation, null space, column space
4/10 4.2, 1.9 Reading injectivity and surjectivity of $T: \mathbb R^n \to \mathbb R^m$ from the matrix
4/12 1.9, 2.1 Matrix Operations
4/14 2.2 Inverse of a Matrix
4/17 2.3 Invertible Matrix Theorem
4/19 4.3 Linear independent sets; bases
4/20 Midterm Exam I 4:30-6:30pm
4/21 2.9 Dimension and rank
4/24 4.4/4.7/5.4 (variant) Coordinates, matrix of a transformation, change of basis
4/26 4.7, 3.1 Determinants and Properties
4/28 3.2 Properties of Determinants
5/1 5.1, 5.2 Eigenvalues and Characteritic Equation
5/3 5.2, 5.3 Characteristic Equation, Diagonalization
5/5 5.3, 5.4 Diagonalization and linear transformations
5/8 4.9,5.8 (optional) Intro to Markov Chains, Iteration Method for Eigenvalues
5/10 6.1-6.2 Inner products and Orthogonality
5/11 Midterm Exam II 4:30-6:30pm
5/12 6.3 Projections
5/15 6.4 Gram-Schmidt Process
5/17 7.1 Diagonalization of Symmetric Matrices
5/19 7.4 Singular Value Decomposition
5/22 7.4 Singular Value Decomposition
5/24 7.4, 7.5 Other applications, e.g., Principal Component Analysis,
SVD and image processing, Facial Recognition, etc.
5/26 Wrap it up
5/29 Memorial Day Holiday No classes
6/1 Final Exam 11:30 am - 2:30 pm

T. R. Shemanske
Last updated July 11, 2017 09:35:00 EDT