ECON 9760 - Machine Learning In Economics

3.0 Credit Hours
Prerequisites: ECON 9710 and ECON ECON 9720 with a grade of C or above.
Description
This course discusses some specific methods of machine learning that are viewed as important for empirical research in economics. These include supervised learning methods for regression and classification, unsupervised learning methods as well as matrix completion methods. There will be particular highlight on newly developed methods at the intersection of machine learning and econometrics that typically perform better than more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of counterfactual effects in structural models.