Machine Learning Foundations for Developers with Michelle Frost

This workshop introduces developers to the fundamentals of machine learning, focusing on practical techniques for understanding, building, and integrating models into applications. Participants will learn the differences between supervised, unsupervised, and semi-supervised learning, along with the essential steps in a machine learning lifecycle including data generation and preprocessing, model building, evaluating, and implementation.

Using this foundational knowledge, we will move into understanding responsible AI (RAI) practices, covering topics like fairness (bias evaluation and mitigation), explainability, and privacy. Attendees will explore actionable strategies to incorporate RAI principles into their workflows, ensuring models are not only functional but also ethical.

Throughout this workshop, we will use hands-on exercises using Python-based tools and provided datasets, with options to work either on personal laptops or hosted environments like Google Colab.