what is finml fr?

FinML is the open course material for a Master's level university module called Big Data in Finance, taught by Dr Daniele Bianchi, covering machine learning methods applied to finance. The course focuses on two core problems: predicting investment returns and assessing credit risk, such as whether a borrower will default on a loan. Each week combines the underlying theory with hands on Python exercises. The repository is organized by week and by material type. There are seven sets of lecture slides built with LaTeX Beamer, seven sets of written lecture notes, quizzes with and without solutions, and one Jupyter notebook tutorial per week. The topics move from the foundations of machine learning such as loss functions and cross validation for time series data, through regression methods like Ridge, Lasso, and gradient boosting, classification methods, unsupervised learning such as PCA and clustering, model interpretability tools like SHAP, and finally neural networks. All of the written materials are LaTeX source files and need a standard TeX distribution such as TeX Live or MiKTeX to compile into PDFs using pdflatex. The datasets used in the exercises are not included in the repository, they are public datasets that need to be downloaded separately from their original sources, with instructions for expected filenames and columns provided in the repository. One notable extra is a small chat tool that lets visitors ask questions about the course material directly in the browser. It is designed to run as a static page on GitHub Pages, and each visitor supplies their own Anthropic or OpenAI API key, which stays only in their browser and is never stored in the repository or sent anywhere except the AI provider they chose. The course materials are released under a Creative Commons license that allows sharing and adapting the content for non commercial purposes only, as long as credit is given and any derivative work uses the same license.