Teaching Machine Learning with No Code

This talk introduces a mobile app to teach, learn and apply Machine Learning, designed to make core algorithms accessible to students and instructors across backgrounds, including those without coding experience. In line with GAISE (2016) recommendations promoting conceptual understanding and hands-on data analysis, the module leverages smartphones and tablets as an immediately available, consistent, and low-barrier technological platform. The app provides interactive implementations of classical ML methods such as Naïve Bayes, Linear Discriminant Analysis, and K-Means Clustering along with built-in datasets or user-uploaded CSV files. Through demonstrations using the Palmer Penguins dataset, the presentation will highlight how the module supports key ML concepts including feature selection, train–test splits, visualization and interpretation of posterior odds, classification boundaries, model accuracy, and prediction for new observations. Attendees will see how the app can serve as the primary technological tool for teaching ML in courses that emphasize interpretation and understanding rather than (or along with) programming. All participants will be able to download the app during the session and will leave with practical strategies for integrating mobile-based ML instruction into their courses.

Machine Learning

Mobile App

Software 

Education & Professional Development

Symposium on Data Science and Statistics (SDSS) 2026