Modern Methods in Statistics, Econometrics, and Machine Learning
Spring 2023
This course provides a brief introduction to a variety of topics in modern statistics, econometrics, and machine learning. Exact topics are to be determined, but may include: neural networks, random forests, text analysis, network analysis, empirical processes, multiple testing, randomization inference, neyman orthogonality, and shape restrictions. Guest lecturers include:
- Whitney Newey, MIT
- Daniel Wilhelm, Ludwig-Maximilians-Universitat Munchen
- Tim Armstrong, USC
- Joe Romano, Stanford University
- Stefan Wager, Stanford University
- Elena Manresa, NYU
- Xiaohong Chen, Yale University
- Andres Santos, UCLA
- Frank Wolak, Stanford