The lecture hours are 3/week, and the estimated private study hours are 8/week. We will cover the following topics and their application to asset allocation problems. Each topic would make up approximately two weeks of lectures and discussion.

1. Least squares


2. Resampling method


3. Latent factor model


4. Regularization


5. Classification


6. Tree models


7. Neural networks


8. Interpretable machine learning

To be announced

1. Homework assignments (70%)
   
   There will be programming assignments every week. A few students will be randomly picked to demonstrate the program and explain their results.


2. Final exam (30%)

Lecture notes will be available on Moodle.

References:

1. James, Witten, Hastie, and Tibshirani (2021) "An Introduction to Statistical Learning: with Applications in R."


2. Murphy (2022) "Probabilistic Machine Learning: An Introduction."


3. Molnar (2022) "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable."