Machine Learning

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.

Lectures

• Linear classifiers: Introduction

• Linear classifiers: Expressiveness

• Videos:

  • [Lecture 1], introducing linear models
  • [Lecture 2], discussing the expressiveness of linear classifiers
  • Older videos:
    • Introduction: pspring 2024, lecture 1], [spring 2023], [fall 2018], [fall 2017]
    • Expressiveness: [spring 2024, lecture 2], [[spring 2023]((https://youtu.be/E6QxpzeQyL4)], [fall 2018], [fall 2017]

Links and Resources

• Chapters 4 and 7 of Hal Daumé III, A Course in Machine Learning