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.
Athena Title
Machine Learn and Predict
Prerequisite
ECON 7710
Grading System
A - F (Traditional)
Student Learning Outcomes
Upon course completion, students will be able to understand the bias-variance trade-off in multivariate regression.
Upon course completion, students will be able to train, test, and deploy supervised machine learning models using common regularization techniques (ridge, LASSO, elastic net) as well as "deep learning" neural nets.
Upon course completion, students will be able to use ML techniques to identify causal variation via synthetic control groups.
Upon course completion, students will be able to reduce high-dimensional problems using unsupervised machine learning: k-means clustering and principal component analysis.
