Mechanistic Interpretability

Catalog description

What is the internal structure of modern neural networks and how can we study it? This course provides a broad and deep introduction to interpretability, the subfield of machine learning concerned with understanding precisely how models process information and why they produce the outputs they do. We will cover topics such as probing, steering, causal abstraction, and sparse autoencoders, with a particular emphasis on causal methods and large language models. The course will include guest lectures from leading interpretability labs across academia and industry.