EE 445

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 Machine Learning

1. Introduction to Machine Learning

Overview of EE 445, and useful information for the course.

Thu, Jan 26 - Sat, Jul 1
Learning conjunctions in a PAC framework

2. Learning conjunctions in a PAC framework

Probabilistic setup for learning using an example

Mon, Jul 12 - Fri, Jul 16
Gaussians

3. Gaussians

Basic on Gaussian Random Variables, univariate and multivariate.

Thu, Mar 2 - Sun, Jul 16
Linear Regression

4. Linear Regression

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

Sun, Feb 12 - Sun, Jul 16
Single neuron networks

5. Single neuron networks

Single Neuron Networks

Mon, Jul 12 - Fri, Jul 16
Support vector machines

6. Support vector machines

Support vector machines

Mon, Jul 12 - Fri, Jul 16
Linear Classifiers

7. Linear Classifiers

This module looks at basic linear classification approaches. First is the natural extension of regression, the Fisher Discriminant. Second is the maximum entropy approach, Logistic Regression. Finally, the maximum margin method, Support Vector Machines.

Mon, Jul 12 - Fri, Jul 16
Neural networks

8. Neural networks

Basic neural network architectures

Mon, Jul 12 - Fri, Jul 16