Math 22: Linear Algebra with Applications

Spring 2026

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.

Lecture Date Section in Text Brief Description
Day 1 Mon. March 30 1.1 Systems of Linear Equations
Day 2 Wed. April 1 1.2 Row Reduction and Echelon Forms
Day 3 Fri. April 3 1.3 1.4 Vector Equations; Matrix Equations
Day 4 Mon. April 6 1.4 -1.5 The Matrix Equation Ax=b and Solution Sets of Linear Equations
Day 5 Wed. April 8 1.7 Linear Independence
Day 6 Fri. April 10 4.1, 4.2 Vector spaces, linear transformation, null space, column space
Day 7 Mon. April 13 4.2, 1.9 Definition of Linear Transformations, Kernel and Range and the standard matrix
Day 8 Wed. April 15 1.9, 2.1 Reading Injectivity and Surjectivity of T: Rn --> Rm from the matrix and Matrix Operations
Day 9 Fri. April 17 2.2 Inverse of a Matrix
Day 10 Mon. April 20 2.3 Inverse Matrix Theorem
Day 11 Wed. April 22 4.3 Linear independent sets; bases
Thrs. April 23 MIDTERM #1
Day 12 Fri. April 24 2.9 Coordinates, Dimension and rank
Day 13 Mon. April 27 4.4/5.4/4.7 Matrix of a transformation, Change of coordinates matrix
Day 14 Wed. April 29 4.7 Change of coordinates matrix and composition of linear transformations
Day 15 Fri. May 1 3.1, 3.2 Determinant and its Properties of Determinants
Day 16 Mon. May 4 5.1, 5.2 Eigenvectors and Eigenspaces
Day 17 Wed. May 6 5.2, 5.3 Eigenvalues and Characteristic Equation
Day 18 Fri. May 8 5.3, 5.4 Diagonalization and linear transformations
Day 19 Mon. May 11 6.1, 6.2 Inner products and Orthogonality
Day 20 Wed. May 13 6.3 Projections
Thrs. May 14 MIDTERM #2
Day 21 Fri. May 15 6.4 Gram-Schmidt Process and Least Squares
Day 22 Mon. May 18 7.1 Diagonalization of Symmetric Matrices
Day 23 Wed. May 20 4.9, 5.8 Intro to Markov Chains, Iteration Method for Eigenvalues
Day 24 Fri. May 22 4.9, 5.8 Application: Markov Chains & Google's page rank
Day 25 Tues. May 26 (x-hour) 7.4 Application: Singular Value decomposition (SVD)
Day 26 Wed. May 27 7.4 Application of SVD to storing images
Day 27 Fri. May 29 No section in book Application -- JPEG compression algorithm
Day 28 Mon. June 3 Section 7.5 Application: Principal Component Analysis (PCA) & Eigenfaces


Page created and maintained by R. Orellana