10-601 Linear Regression

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

Slides

Readings

  • Mitchell 4.1-4.3
  • Optional:
    • Bishop 3.1

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
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