Lecture 
Date 
Section in Text

Brief Description

Day 1 
Wed. Sept 16

1.1

Systems of Linear Equations

Day 2 
Thurs. Sept 17

1.2

Row Reduction and Echelon Forms

Day 3 
Fri. Sept. 18

1.3

Vector Equations

Day 4 
Mon. Sept. 21

1.4 1.5

The Matrix Equation Ax=b and Solution Sets of Linear Equations

Day 5 
Wed. Sept. 23

1.7

Linear Independence

Day 6 
Thurs. Sept. 24

1.8

Introduction to Linear Transformations

Day 7 
Fri. Sept. 25

1.9

The Matrix of a Linear Transformation

Day 8 
Mon. Sept. 28

2.1

Matrix Operations

Day 9 
Wed. Sept. 30

2.2

Inverse of a Matrix

Day 10 
Thurs. Oct. 1 
2.3

Characterization of Invertible Matrices

Day 11 
Fri. Oct. 2 
3.1  3.2 
The Determinant and its Properties 
Day 12 
Mon. Oct. 5

4.1 
Vector Space and Subspaces

Day 13 
Wed. Oct. 7 
4.2

Null Spaces (Kernel), Column Spaces, and Linear Transformations

Day 14 
Fri. Oct. 8 
4.3

Linear Independent sets and bases

Day 15 
Fri. Oct. 9 
4.4

Coordinate Systems

Day 16 
Mon. Oct. 12 
4.5

The dimension of a vector space

Day 17 
Wed. Oct. 14 
4.6

Rank

Day 18 
Fri. Oct. 16

4.7

Change of basis

Day 19 
Mon. Oct. 19 
5.1

Eigenvectors and Eigenvalues

Day 20 
Wed. Oct. 21 
5.2

The Characteristic Equation

Day 21 
Thurs. Oct. 22 
5.3

Diagonalization

Day 22 
Fri. Oct. 23 
6.1

Inner Product, Length and Orthogonality

Day 23 
Mon. Oct. 26

6.2

Orthogonal Sets

Day 24

Wed. Oct. 28

6.3

Orthogonal Projections

Day 25 
Thurs. Oct. 29 
6.4

The GramSchmidt Process

Day 26 
Fri. Oct. 30 
7.1

Diagonalization of Symmetric Matrices

Day 27 
Mon. Nov. 2 
4.9

Application: Markov Chains & Google's page rank

Day 28 
Wed. Nov. 4 
7.5

Principal Component Analysis (PCA) & Eigenfaces

Day 29 
Fri. Nov. 6 
7.4

Application: Singular Value decomposition (SVD)

Day 30 
Mon. Nov. 9 
7.4

Application: SVD and storing images

Day 31 
Wed. Nov 11 
Guess speaker

Application: Image processing

Day 32 
Fri. Nov 13 
No section in book

Application  JPEG compression algorithm.

Day 33 
Mon. Nov. 16 
no new material

Review
