Lecture Notes

Lecture Slides 1

Lecture 1: Introduction to Machine Learning

Lecture Slides 2

Lecture 2: Python Review & Launch your own AI assistant

Lecture Slides 3

Lecture 3: The Case for Financial Machine Learning

Lecture Slides 4

Lecture 4: The Virtues of Complex Models, covers shrinkage methods and basic machine learning concepts

Lecture Slides 5

Lecture 5: Return Prediction Part I, covers penalized linear models, dimension reduction, tree models, ensemble learning

Lecture Slides 6

Lecture 6: Return Prediction Part II, covers fully connected neural network, convolutional neural network, transformer, large language model and its local deployment

Lecture Slides 7

Lecture 7: Recent Development, covers reinforcement learning (RL)

Class Requirements

Reference Paper
Kelly, B., & Xiu, D. (2023). Financial machine learning. Foundations and Trends® in Finance, 13(3-4), 205-363. link

Project & Presentation (50%)
Students are required to form study groups, each consisting of 2 to 3 members, and collaboratively work on a project on Kaggle. At the end of this semester, each group needs to present their findings and project outcomes.

Final Report (50%)
Each study group needs to submit a report detailing the application of machine learning models to their Kaggle project. Additionally, please clearly state in your report each team member’s contribution to this project. (under 20 pages, Times New Roman, 12pt, 1.5 line space)

Final Report DUE DATE: TBD
Please submit your report here: TBD