ID:
510874
Durata (ore):
22
CFU:
3
SSD:
STATISTICA
Anno:
2024
Dati Generali
Periodo di attività
Primo Semestre (23/09/2024 - 20/12/2024)
Syllabus
Obiettivi Formativi
The Financial Learning module introduces how learning and decision making can be practically applied to financial problems. As statistics has become a valuable asset in the financial domain, the course lectures aims at addressing to the illustration and discussion of the basic and advanced algorithms and models, both on the theoretical and practical view point. Specifically, the students will learn how to deal with: 1) the main (white-box) statistical models; 2) the model selection procedure (whose purpose is selecting the best model among alternative models); 3) the most commonly used (black-box) machine learning models. In addition, students will achieve expertise on the recent research developments in the domain of trustworthy Artificial Intelligence (AI): they will be able to exploit the most innovative metrics in order to evaluate the safety of AI methods. Dataset examples will be presented to give students an appreciation for the application of the methodologies to different settings. In this way, the theoretical approaches will be combined with the practical framework, allowing the students to reach an adequate knowledge of the R coding.
Students will face the extension of the aforementioned topics to the Bayesian framework, which will represent the focus of the Bayesian Learning module.
Students will face the extension of the aforementioned topics to the Bayesian framework, which will represent the focus of the Bayesian Learning module.
Prerequisiti
Basic Knowledge of Statistics and/or Probability;
Basic Knowledge of Coding.
Basic Knowledge of Coding.
Metodi didattici
The course will be structured through theoretical lectures combined with financial data analysis lead by means of the RStudio software.
Each lecture will be split in two parts: the first part will be devoted to the introduction of the theoretical framework underlying the presented models, while the second part will be addressed to the implementation of the models by means of the RStudio statistical software and the use of real data.
With regard to the topics related to the recent developments in the AI field, the practical part will be faced through simulated data in Rstudio.
Each lecture will be split in two parts: the first part will be devoted to the introduction of the theoretical framework underlying the presented models, while the second part will be addressed to the implementation of the models by means of the RStudio statistical software and the use of real data.
With regard to the topics related to the recent developments in the AI field, the practical part will be faced through simulated data in Rstudio.
Verifica Apprendimento
The examination procedure will be based on an oral slide-presentation and a discussion of the main data analysis results.
Each student will choose one of the proposed datasets and, based on the selected data, will set a specific research question and will lead an analysis through the RStudio software.
Each student will choose one of the proposed datasets and, based on the selected data, will set a specific research question and will lead an analysis through the RStudio software.
Testi
Materials and bibliographic suggestions will be provided along the course (data, code and papers supplied in class).
Possible bibliographic references for the Financial Learning module are:
- Gareth J., Witten D., Hastie T., Tibshirani R. (2023), 2nd Edition, Springer, downloadable from the website: https://www.statlearning.com.
- Giudici P. (2003): Applied Data Mining - Statistical Methods for Business and Industry, 1st Edition, Wiley, available at: https://theswissbay.ch/pdf/Gentoomen%20Library/ Data%20Mining/Applied%20Data%20Mining-Statistical% 20Methods%20for%20Business%20and%20Industry_ Giudici%20P%20%282003%29.pdf.
Possible bibliographic references for the Bayesian Learning course are:
- 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.
Possible bibliographic references for the Financial Learning module are:
- Gareth J., Witten D., Hastie T., Tibshirani R. (2023), 2nd Edition, Springer, downloadable from the website: https://www.statlearning.com.
- Giudici P. (2003): Applied Data Mining - Statistical Methods for Business and Industry, 1st Edition, Wiley, available at: https://theswissbay.ch/pdf/Gentoomen%20Library/ Data%20Mining/Applied%20Data%20Mining-Statistical% 20Methods%20for%20Business%20and%20Industry_ Giudici%20P%20%282003%29.pdf.
Possible bibliographic references for the Bayesian Learning course are:
- 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.
Contenuti
The course will introduce and illustrates the mathematical framework underlying the basic statistical and machine learning models.
The first three lectures will be focused on the presentation and discussion of: 1) linear regression models; 2) logistic regression models; 3) model selection procedures.
The last three lectures will be focused on the presentation and discussion of: 1) neural network models; 2) tree models and Random Forest models; 3) the recent approaches in the AI field.
Application to credit lending data and financial market data will be considered.
The first three lectures will be focused on the presentation and discussion of: 1) linear regression models; 2) logistic regression models; 3) model selection procedures.
The last three lectures will be focused on the presentation and discussion of: 1) neural network models; 2) tree models and Random Forest models; 3) the recent approaches in the AI field.
Application to credit lending data and financial market data will be considered.
Lingua Insegnamento
INGLESE
Corsi
Corsi
FINANCE
Laurea Magistrale
2 anni
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