Location: Nueces A (The Commons)

Description: In this tutorial participants will gain an understanding of how to construct and evaluate predictive models. Supervised machine learning algorithms (classification and regression) are often used in learning analytics to make predictions of student outcomes in support of timely interventions by instructors, advisors, or automated systems.

Activities: 

1. Explore use cases for predictive analytics

2. Discuss ethical and practical considerations in predictive analytics

3. Build, train, and evaluate classification and regression models in Python using Scikit-Learn

Takeaways: 

By the end of the workshop, participants will have:

1. Knowledge of the utility and limitations of predictive models
2. The ability to interpret and converse about evaluation measures of predictive models
3. An understanding of how data sets are used to build and evaluate predictive models

Intended Audience:

Participants should be familiar with basic introductory statistics. No programming experience is necessary. This session is particularly aimed at individuals who do not have experience with predictive modeling techniques.

Tutorial Leader: Craig Thompson, University of British Columbia