Summary

This week, we will discuss linear regression, our first parametric model for regression. We will also introduce regularization as a method to control the complexity of (linear) models.

Learning Objectives

After completing this week, you are expected to be able to:

  • Differentiate between parametric and nonparametric regression.
  • Use sklearn to fit linear regression models and make predictions for unseen data.
  • Preprocess data to add polynomial and interaction terms for use in linear models.
  • Understand what makes linear models linear and how both linear regression and logistic regression are linear models.
  • Understand how the ridge and lasso constraints lead to shrunken and spare estimates.
  • Use ridge regression to perform regression and classification.
  • Use lasso to perform regression and classification.

Topics

  • Linear Regression
    • Parametric Models
    • Polynomial and Interaction Terms
  • Regularization
    • Lasso
    • Ridge

Activities

Upcoming Deadlines