The financial learning module introduces how learning and decision making can be practically applied to financial problems. Specifically, it will first introduce regression, tree and neural network models, and compare them with Bayesian networks, using the R software. It will then compare the models in terms of machine learning requirements such as predictive accuracy, robustness, explainability and fairness, by means of hands-on R coding, and real data arising from lending markets, financial markets and cyber security data.
Prerequisiti
Basic Knowledge of Statistics and/or Probability; Basic Knowledge of Coding.
Metodi didattici
Theoretical Lectures combined with financial data analysis lead by means of the R software.
Verifica Apprendimento
Oral Discussion
Testi
Banks, D., Rios, J., and Rios Insua, D. (2015). Adversarial Risk Analysis (Vol. 343). CRC Press. - O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E, and Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. - Rios Insua, D. and Ruggeri, F. Eds. (2000), Robust Bayesian Analysis, Lecture Notes in Statistics, vol. 152, Springer. - Materials: data, code and papers supplied in class for the financial learning part
Contenuti
Regression, Tree and Neural Network models. Model comparison in terms of Robustness, Accuracy, Fairness and Explainability. Application to credit lending data, financial market data, cyber security data.