10-601 Linear Regression

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

• William's lecture: Slides in Powerpoint, in PDF.
• Side note: The bias-variance decomposition.

Readings

• Mitchell 4.1-4.3
• Murphy: 7.1-7.3, 7.5.1
• Optional:
  • Bishop 3.1
  • There's also a nice but somewhat less technical video lecture on overfitting and bias-variance

What You Should Know Afterward

• Regression vs. classification
• Solving regression problems with 1 and 2 variables
• Ordinary least squares (OLS) solution (aka normal equations) to linear regression problems
• Gradient descent approach to linear regression
• Data transformation and its impact on the way linear regression is solved, and the expressiveness of LR models