Intro to ML Safety (Spring 2023)

Applications to this course have closed

Introduction to ML Safety is an 8-week course that aims to introduce students with a deep learning background to empirical AI Safety research. The program is designed and taught by Dan Hendrycks, a UC Berkeley ML PhD and director of the Center for AI Safety, and provides an introduction to robustness, alignment, monitoring, systemic safety, and conceptual foundations for existential risk.

Each week, participants will be assigned readings, lecture videos, and required homework and coding tasks. The materials are publicly available at course.mlsafety.org.

The course will be virtual by default, though in-person sections may be offered at some universities.

The prerequisites for the course are:

• Familiarity with deep learning (e.g. a college course). Watch this deep learning review to check your level of knowledge.
• Linear algebra or introductory statistics (e.g., AP Statistics)
• Multivariate differential calculus

If you are not sure whether you meet these prerequisites, err on the side of applying. We will review applications on a case-by-case basis.

For more information about the program, visit: mlsafety.org/intro-to-ml-safety