FIN40090

Advanced Topics course in Modeling Financial Data and using the result for Investing and Decisionmaking. The topics typically focus on Managing and Modeling Financial Series data, Classification, Forecasting, and Optimisation Methods, with an emphasis on Statistical Reliability of forecasts. Specific tools and methods include Bayesian Decision Theory, Linear Discriminant Analysis, Support Vector Machines, Neural Networks, Genetic Algorithms/Optimisation, Regularisation and Resampling Methods, Regression and Classification Trees and Forests, Deep Learning, and some Information-Theoretic criteria for combining forecasting and investing.
A section on Large Language Models (LLM's) such as ChatGPT will be include (source: TBA)

Next-Generation frontiers such as Quantum Machine Learning may be discussed at an abstract level.

Computer platforms utilised will be Julia (a freely downloadable package for numerical computation and machine learning, with syntax very similar to MATLAB) and either Python or the statistical computing package R (where previous knowledge of these languages is not assumed except possibly Python).