| Course ID: | ECON 7720. 1.5 hours. |
| Course Title: | Machine Learning and Prediction |
Course
Description: | An introduction to machine learning (ML), a research field at the intersection of economics, statistics, and computer science, for business analytics students. Focus will be on the development of "trained" models to represent patterns embedded in large, high-dimensional data using supervised and unsupervised machine learning techniques. Such models enable leadership in business and governmental organizations to leverage their data to make better decisions. |
| Oasis Title: | Machine Learn and Predict |
| Prerequisite: | ECON 7710 |
| Grading System: | A-F (Traditional) |
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| Course Objectives: | Upon course completion, students will be able to:
1. Understand the bias-variance trade-off in multivariate regression.
2. Train, test, and deploy supervised machine learning models using common regularization techniques (ridge, LASSO, elastic net) as well as "deep learning" neural nets.
3. Use ML techniques to identify causal variation via synthetic control groups.
4. Reduce high-dimensional problems using unsupervised machine learning: k-means clustering and principal component analysis. |
| Topical Outline: | 1. Python Refresher
2. Multivariate Linear Regression
3. Supervised Learning
4. Synthetic Controls
5. Unsupervised Learning |