Chicago Booth Machine Learning (ML) in Finance Reading Group

The Chicago Booth Machine Learning in Finance Reading Group is a student-run organization for doctoral students to explore cutting edge topics in the machine learning applications in the academic finance literature.

The group is open to PhD students and faculty.

• Faculty Organizer: Stefan Nagel
• Student Coordinators (2022-23): Sangmin Oh

Schedule & Logistics

Current Schedule: Spring 2023

Past Sessions

• Winter 2023
• Fall 2022
• Spring 2022
• Winter 2022
• Fall 2021
• Spring 2021
• Winter 2021
• Fall 2020

Resources

General

• High-Dimensional Probability for Data Science by Roman Vershynin (UC Irvine)
• Econometrics IV by Francis J. DiTraglia (UPenn)
• How to avoid machine learning pitfalls by Michael A. Lones
• Machine Learning for Economists (57750) by Itamar Caspi

Natural Langauge Processing

• Stanford Course on NLP with Deep Learning
• Nice overview of what can be done with NLP
• The clearest overview of how ChatGPT works by Stephen Wolfram
• GPT in 60 Lines of NumPy

Reinforcement Learning

• Dimitri Bertsekas course on RL and optimal control

Econometrics

• Causal Inference from ML Perspective
• Machine Learning for Econometrics by Christophe Gaillac and Jereemy L’Hour