94-989 Machine Learning in Practice

12 units, Carnegie Mellon University

This is a project-based course designed to provide training and experience in solving real-world problems using machine learning, with a focus on problems from public policy and social good. Through lectures, discussions, readings, and project assignments, students will learn about and get hands-on experience building end-to-end machine learning systems, starting from project definition and scoping, to modeling, to field validation and turning their analysis into action. Through the course, students will develop skills in problem formulation, working with messy data, communicating about machine learning with non-technical stakeholders, model interpretability, understanding and mitigating algorithmic bias & disparities, evaluating the impact of deployed models, and understanding the ethical implications of design choices made throughout the ML pipeline.

Prerequisites: none listed

Offered: Fall 2026, Fall 2025

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