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This course provides a broad perspective on theory of Machine Learning methods and its application in finance. This course covers supervised learning for regression and classification problems. The theoretical part of the course (first four classes) consists of the basis of machine learning including measuring performance, model testing, details of validation methods, feature engineering and selection, simple linear and logistic regression, discriminant analysis as well as K-nearest neighbours, Support Vector Machines, ridge and Lasso regression modelling methods, decision trees and random forest. Then in the practical part, each lecture tackles a particular financial problem faced by modellers and showcases an ML solution to it. The solutions focus on the end-to-end process, including data handling and feature generation as well as techniques for gaining executive support. |
- Teacher: Szymon Lis
- Teacher: Michał Woźniak