Machine Learning Foundations for Developers
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.
Michelle Frost is an AI Advocate at JetBrains. With over a decade of engineering experience, Michelle holds a Bachelor of Science in Computer Science from the University of Missouri at Kansas City, a Master of Science in Artificial Intelligence from Johns Hopkins University, and is a Microsoft AI MVP. As an established AI and Machine Learning specialist, Michelle focuses on Responsible AI development. Her approach is grounded in creating AI that is fair, accountable, and transparent.
Michelle is also an active member and Tech Advisor to The Center for Practical Bioethics' Ethical AI Initiative. When not behind the screen, she can be found tending to her garden with her 100 lb pup Wilbur by her side.