3️⃣CFRM 521: Machine Learning in Finance

To be frank, this course made me feel like a computer science major. You can't do machine learning without learning about the machines, so the entirety of the course was spent in VSCode, typing away.

Reading Material:

• Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow, O’Reilly, 2022.

This course is an introduction to machine learning for an audience with a background in quantitative finance. The course takes a hands-on approach and favors gaining working knowledge of several major machine learning techniques rather than focusing on a handful of methods. Students learn to use well-tested and widely used Python implementations of machine learning algorithms instead of coding them from scratch. The following topics will be covered: an overview of classification and regression techniques, support vector machines, decision trees, random forests, and dimensionality reduction. We also discuss selected topics in unsupervised learning and artificial neural networks.

1. Training Model & Regression

2. Classification

3. Time Series

4. Support Vector Machines

5. Decision Trees

6. Ensemble Learning, Random Forests

7. KNN, PCA, Kernel PCAm LLE

8. K-Means Clustering

9. Training Deep NN, Recurrent NN, Convolutional NN