Teaching

Machine learning for Social Science

This course provides an overview of key machine learning (ML) concepts and tools relevant to social science research. It begins with a general introduction to ML, reviewing foundational ideas and contrasting them with traditional statistics. Central techniques in supervised and unsupervised learning are introduced. Building on these foundations, later modules cover specialized topics such as convolutional neural networks and image as data, word embeddings and text as data, and the use of machine learning for causal inference. In computer labs, students learn to apply these techniques in statistical software to solve practical problems in social science research.