Quantitative Data Analysis in Finance

The course is a broad introduction to the techniques of machine learning in context of quantitative finance. Topics include parametric & non-parametric regression, supervised learning and natural language processing & image recognition techniques from computer science to collect new insights from unstructured text & image data. Methods covered include regularized linear models in high dimensions (LASSO family) dimension reduction techniques, Ensemble methods (Bagging & Boosting) Regression Trees/Random Forests/Boosted Trees, Neural Networks/Deep Learning, Classification methods, Clustering & text analysis. Examples take from financial models.