In this one-day course, after an introductory lecture on Machine Learning for the social sciences, two related Stata modules, r_ml_stata_cv and c_ml_stata_cv will be illustrated for fitting popular Machine Learning (ML) methods both in a regression and a classification setting.
Using the recent Stata/Python integration platform (SFI) introduced in Stata 16, these commands provide hyper-parameters’ optimal tuning via K-fold cross-validation using grid search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction. Applications will be carried out on RISIS datasets.

This course  targets researchers aiming to master tools and methodologies to dig into Machine Learn applications. It will involve up to 15 participants. It is addressed to:

-Senior scientists, early career researchers and PhD students

-Policymakers at the local, regional, national and international level (e.g., European Commission)

-Research associations

Participants should have some basic notions of basic statistics (mainly regression models), a working knowledge of Stata, and some practice with STI analysis.

It is not necessary for participants to have knowledge of Python.