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Machine Learning Short Course

August 5-7, 2019, 9am-5pm
Cornell University, Ithaca, NY
Mann Library B30A

Registration Fee

Current Cornell faculty, staff, and students: $550.00
All other participants: $650.00

Files: Data Files

Registration for this course is now closed.

Workshop Overview

Machine learning refers to the use of computational tools and statistical learning to understand large and complex datasets. The goal of most machine learning methods is to identify patterns in the data, simplify data structure by performing data reduction, create models or algorithms that can be used to make highly accurate predictions, or to accurately classify items into categories. This course will provide an introduction to the practice of machine learning, as well as review several common methods. This course focuses heavily on application, so a majority of instruction will be focused on the implementation of these methods.

Researchers from all fields are welcome. This course will provide an introduction to the topics listed below and provide attendees with the knowledge and skills necessary to confidently apply these methods to their own research. No previous background in machine learning is necessary, but we will assume an understanding of multiple linear regression.

Specific topics to be covered include:

  • Overview of machine learning methods
  • Supervised vs unsupervised learning
  • Model evaluation, testing and training, cross validation
  • Shrinkage models: lasso, ridge regression and elastic net
  • Principal components regression
  • Partial least squares
  • Logistic regresion
  • Linear discriminant analysis
  • Classification and regression trees
  • Random forests
  • Principal components analysis
  • Cluster analysis

Statistical Software

This course will have a substantial hands-on component using R via RStudio. Participants should have a working knowledge of both R and RStudio (e.g., be comfortable importing and manipulating data, performing simple statistical analyses, and fitting linear regression models). Prior to the Machine Learning short course, participants will be given an opportunity to register for a free introduction to R webinar that will cover the material that participants are expected to be familiar with.

All sessions will be held in a computer lab with PCs that have the most recent versions of R and RStudio installed. Participants are also welcome to bring their own laptop to use during the course, but please make sure to have the most recent version of both R and RStudio already installed. To download and install R, visit and to download and install RStudio, visit


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If you have any questions about this short-course, please contact us at