10-601 Linear Regression

From Cohen Courses
Jump to navigationJump to search

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



  • 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