Statistical learning

Room 104, Stuart Hall, Chicago, IL


  • Review the major goals of statistical learning
  • Explain the difference between parametric and non-parametric methods
  • Introduce linear models and ordinary least squares regression
  • Demonstrate how to estimate a linear model in R using lm()
  • Demonstrate how to extract model statistics using broom and modelr
  • Practice estimating and interpreting linear models
  • Demonstrate the use of logistic regression for classification
  • Identify methods for assessing classification model accuracy

Before class

  • Read chapters 22-25 in R for Data Science
  • This is not a math/stats class. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:
For those with little/no statistics training
  • Chapters 7-8 of OpenIntro Statistics - an open-source statistics textbook written at the level of an introductory undergraduate course on statistics
For those with prior statistics training

Class materials

What you need to do

Benjamin Soltoff
Assistant Instructional Professor in Computational Social Science