This module discusses modern methods from Computer Science and Statistics for the analysis of large datasets. Those “Machine Learning” or “Statistical Learning” methods have become essential tools for the exploration and evaluation of data for scientists and practitioners in many fields, including Economics and Finance. Various approaches for data inspection, inference and prediction will be discussed both from a theoretical and applied perspective. The course will in particular cover cluster analysis, dimensional reduction methods (like LASSO and principal components), artificial neural networks, and other related topics.
Upon successful completion of the module, a student will be able to gain deeper knowledge on how to manage and analyse big data.
- practical implementation of machine learning and dimensional reduction methods and their applications in asset pricing and risk management. understanding an
- interpreting the results in a way that is useful to the end user.
- understanding the main theoretical concepts behind those methods.
- data inspection, inference, prediction
- basics machine learning methods
- dimensional reduction, k-means clustering, lasso
- decision trees, boosting, bagging, support vector machines.
80% for group coursework, 20% for online individual exam.
Current students should refer to Moodle for specific details of the current year’s assessment.
Lecture notes will be provided during the course. Two suggested introductory books are below. Those are freely available online.
- G. James, D. Witten, T. Hastie and R. Tibshirani: “An Introduction to Statistical Learning”, 2013 (available online: http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)
- F. Chollet: “Deep Learning with Python”, 2018 (available online: http://faculty.neu.edu.cn/yury/AAI/Textbook/Deep%20Learning%20with%20Python.pdf)