The repo contains the material for the short course entitled "Machine Learning for the Social Sciences", which I have taught to the students of the Doctoral Programme in Sociology and Social Research at the University of Trento.
The content of the slides is largely based on the previous edition of the course taught by Gian Maria Campedelli.
Course overview:
- Lesson 1: introduction to ML, a brief field history, and implications for the social sciences. The two cultures: prediction vs. explanation. → slides
- Lesson 2: intro to technical concepts of ML. Generalizability and model evaluation. Basic examples of ML algorithms. → slides
- Lesson 3: bias and fairness in ML. The lecture is mainly based on the material created by Anna Sapienza for the SocialComQuant Summer School 2023.