Fairness and Explainability Lectures
Two lectures (3h each) introduce the risks of big data and AI, the discovery of discrimination, the design of fair classifiers, and the topic of explainability in AI.
- Part 1: slides and recordings (large file)
- Part 2: slides and recordings (large file)
- Jupyter notebooks
Statistical Methods for Data Science
A graduate course (48 hours) introducing inferential statistics for data scientists using R. Recordings of lectures available.
Contacts@UNIPI Prof. Salvatore Ruggieri NoBIAS ITN has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 860630.
salvatore.ruggieri@unipi.it
http://pages.di.unipi.it/ruggieri/
Ph.: +39 050 2212782
