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A A E 722

MACHINE LEARNING IN APPLIED ECONOMIC ANALYSIS

4 credits.

The basic methods, implementation and applications of machine learning for understanding contemporary economic issues using large data sets. Building upon understanding of standard econometric models, the topics include data mining techniques; regression model selection and regularization; post selection inference and economic applications; tree-based methods; neural networks; random forests and casual inference; and unsupervised learning.

Topics

Topics explicitly listed in official catalogue description

Prerequisites & course information

Requisites: A A E 636 or ECON 704 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Describe and explain the mechanics of basic machine learning methods Audience: Graduate 2. Employ data exploration and visualization tools for analyzing large amounts of data Audience: Graduate 3. Select model and conduct post-selection inference of high-dimensional data Audience: Graduate 4. Apply machine learning methods on large data sets for economic and policy analysis Audience: Graduate 5. Demonstrate the ability to use statistical packages for methods covered in the course Audience: Graduate Learning Outcomes: 1. Describe and explain the mechanics of basic machine learning methods Audience: Graduate 2. Employ data exploration and visualization tools for analyzing large amounts of data Audience: Graduate 3. Select model and conduct post-selection inference of high-dimensional data Audience: Graduate 4. Apply machine learning methods on large data sets for economic and policy analysis Audience: Graduate 5. Demonstrate the ability to use statistical packages for methods covered in the course Audience: Graduate

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