Teaching Machine Learning to Non-Experts through Interactive Algorithm Visualizations

Lis Sulmont, lis@cim.mcgill.ca
Supervised by Jeremy Cooperstock and Elizabeth Patitsas

Photos from Unsplash.

 

Project Description

Machine learning has become an important topic for students outside of computer science and statistics to understand because of its useful applications and its societal impacts. This project explores making machine learning more accessible through HCI methods, specifically for those with non-technical backgrounds. The first component of this project interviewed instructors who have taught machine learning to non-computer science major students (see publications). Based on this, we are currently designing an interactive algorithm visualization to explain the bias-variance tradeoff, a topic instructors find difficult to teach. Studies will be conducted to evaluate the effectiveness of interactive algorithm visualizations for teaching non-experts machine learning.

 

Publications

Teaching Machine Learning to Non-Majors: An Exploration of Pedagogical Content Knowledge (to appear). Elisabeth Sulmont, Elizabeth Patitsas and Jeremy Cooperstock. ACM Technical Symposium on Computer Science Education. Minneapolis, Minnesota, February 2019.

 


Last updated: November 5, 2018