Course Catalog
Machine Learning for Public Policy Lab
94-889
Units: 12
Description
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
Learning Outcomes
- build end-to-end machine learning systems, -
- 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.
- communicate findings to both policy and technical audiences
Prerequisites Description
Students will be expected to know Python (for data analysis and machine learning),SQL, and have prior graduate coursework in machine learning. This course assumes that you have taken graduate Machine Learning courses before and is focused on teaching how to use ML to solve real-world problems. Experience with *nix command line, git(hub), and working on remote machines will be helpful and is highly recommended.