Part of the Digital Finance MSCA programme.
| Lecture | Data Preprocessing, Feature Engineering |
| Workshop | Exploratory Data Analysis, "Whiteboard" discussion with open problems |
| Instructor | Tommaso Guerrini |
| Lecture | Hands-on Intro to Tabular Data Modeling |
| Workshop | Probabilistic binary classification |
| Instructor | Tommaso Guerrini |
| Lecture | Credit Risk Modeling on Fixed Income Securities |
| Workshop | Structuring a Securitization |
| Instructor | Stefano Penazzi |
| Lecture | Intro to MLOps Reproducibility and Model Monitoring |
| Workshop | Project group work / preparation for early project checkpoint on Day 5 |
| Instructor | Gennaro Di Brino |
Early Feedback Checkpoint – remote friendly: Student presentations and Demos.
See SETUP.md for environment setup instructions (Python, dependencies, JupyterLab).
The course assessment will challenge students to go through a machine learning project from start to finish on a given dataset related to credit markets and/or structured finance.
Candidates will be assessed based on the group project in a pass/fail way, with the evaluation encompassing the quality of the code (including the documentation), the final presentation and the paper. In case of insufficient project evaluation, a single make-up session is provided.
The final assessment will take place approximately 4 months after the end of the course, and will consist of a presentation including Q&A and deep-dive in the codebase. Monthly project-progress meetings will be organized to assess students' progress and address questions they may have.
