Basics of Statistical Learning
Welcome to the Spring 2021 semester of STAT 432, Basics of Statistical Learning, sections 1UG and 1GR, at the University of Illinois at Urbana-Champaign.
STAT 432 provides a broad overview of machine learning, through the eyes of a statistician. As a first course in machine learning, core ideas are stressed, and specific details are de-emphasized. After completing the course, students should be able to train and evaluate statistical models. While we will not discuss an exhaustive list of methods, given the framework developed throughout the course, students should feel comfortable exploring new methods and models on their own. Previous experience with R programming is necessary for success in the course as students will be tested on their ability to use the methods discussed through the use of a statistical computing environment.
Almost all course information can be found on this website. We will use three additional external sites:
Additional information about both resources can be found in the Syllabus! If this is your first time on this website, that should be the first thing you read. If you’re a student progressing through the course, you’ll find the information for each week in the links to the left or in the burger menu. The numbered links correspond to the weeks of the course.