Quantitative Data Analysis in Finance

The course gives a broad introduction to the techniques of machine learning, and places those techniques within the context of computational finance. Topics include parametric and non-parametric regression, and supervised learning techniques. Methods covered include regularized linear models in high dimensions (LASSO family), Ensemble methods (Bagging and Boosting), Regression Trees/Random Forests/Boosted Trees, Neural Networks/Deep Learning, Classification methods, Clustering. We also discuss the implementation of dimension reduction techniques, including principal components analysis. Examples are taken from financial models.