Machine learning has become an increasingly integral part of public policies. It is applied to policy problems that do not require causal inference but instead require predictive inference. Solving these prediction policy problems requires tools that are tuned to minimizing prediction errors, but also frameworks to ensure that models are efficient and fair.
The Machine Learning for Policy course will introduce the theory and applications of machine learning algorithms, with a focus on policy applications and issues.
The first edition from this course was offered last year (2024). We are currently organizing this year’s edition, which is scheduled for July. To stay updated, we encourage you to follow UNU-MERIT's LinkedIn page, where we will share announcements about the course and information regarding the application.
Meanwhile, access the course content from last year's edition here: https://www.ml4publicpolicy.com/intro.html
