AI: Topics in Machine Learning (Machine Learning and Data Science)

This course introduces foundational principles and practical applications of machine learning for policy analysis and data-driven research. Students build on prior training in probability and statistics to examine algorithmic workflows, model development, and evaluation techniques. The course covers supervised and unsupervised learning, performance metrics, and common sources of bias and failure.

Through applied projects, students complete the full life cycle of an ML analysis—from data preparation and feature engineering to communication of results—developing a portfolio that demonstrates competency in modern AI and data science methods.