This lecture is the first of several lectures dealing with linear classifiers. The linear model is the most popular classifier and over the course of many lectures in the class, we will see different learning algorithms for this hypothesis class.
This lecture defines a linear classifier and points out that it is an expressive hypothesis class.
We will also see the first of many algorithms for learning linear models – the least mean square method for linear regression.
Lectures
Links and Resources

Chapters 3 and 6 of Hal Daumé III, A Course in Machine Learning

Chapter 3.1 of Christopher Bishop, Pattern Recognition and Machine Learning.