Machine Learning for Economists

Undergraduate / Graduate

This course reviews modern machine learning techniques and their applications in economics. The course covers some of the classical techniques, including lasso, regression trees, random forests, principal components analysis, and neural networks, as well as cutting-edge double machine learning methods. Applications include economic models of network formation, program evaluation, demand estimation, and asset pricing. The course involves programming and working with data. Students are expected to have a solid background in statistics, econometrics, and computation.