Empirical Technology and Operations Management Course

I taught a set of lectures on "Introduction to Machine Learning for Social Scientists" as part of this required course for first year PhD students. This module familiarizes students with all the basic concepts in machine learning, their implementations, as well as the real world challenges encountered when building and deploying machine learning models. 

Topics encompass:

Supervised learning
Unsupervised learning
Reinforcement learning
Practical Challenges: overfitting, data imbalance, missing counterfactuals
Making machine learning models fair, interpretable, and robust