EE 345

Modules Topics covered in this class

For any module not marked as “Coming Soon”, click on it to go to a page containing details.

Introduction to EE 345/L

1. Introduction to EE 345/L

Overview of EE 345/L, and some simple initial tasks to get oriented and ready for the course. View the full comic here. Syllabus here

Mon, Aug 21 - Tue, Aug 22
Vectors and Linear combinations

2. Vectors and Linear combinations

Vectors and Linear combinations

Wed, Aug 23 - Wed, Aug 30
Matrix Multiplication

3. Matrix Multiplication

Photo: Josh Alman and Virginia Williams, record holders for fastest matrix multiplication algorithm. Our hope is to find a multiplication algorithm essentially as fast as just writing out the matrices that go into the product.

Wed, Aug 30 - Thu, Sep 7
Gaussian Elimination

4. Gaussian Elimination

Gaussian Elimination, Solutions of linear equations

Tue, Sep 5 - Tue, Sep 12
Inverses

5. Inverses

Inverses, an undo operation for matrices

(Icon created by Creatype - Flaticon)

Wed, Sep 13 - Thu, Sep 21
Linear Spaces

6. Linear Spaces

Four fundamental linear spaces of a matrix

Fri, Sep 22 - Mon, Oct 2
Linear Independence

7. Linear Independence

Linear independence. This crucial property generalizes into matroids, the structure underlying all greedy algorithms that are also optimal. The picture is a Fano matroid, a confluence of matroids, linear independence, and projective geometry.

Tue, Oct 3 - Wed, Oct 11
Bases and dimension

8. Bases and dimension

Bases and Dimension

Thu, Oct 12 - Thu, Oct 19
Linear Regression

9. Linear Regression

Maximum Likelihood (Ordinary Least Squares) and Bayesian formulations of Linear Regression, geometry and significance

Wed, Oct 25 - Mon, Nov 6
Determinants

10. Determinants

Determinants

Wed, Nov 8 - Tue, Nov 14
Spectral Decomposition

11. Spectral Decomposition

Eignevalues, Eigenvectors and Spectral Decomposition

Wed, Nov 15 - Wed, Nov 22