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# MIT6.S184: Generative AI with Stochastic Differential Equations

## Course Introduction

- University: MIT
- Prerequisites: Basic understanding of deep learning, and be comfortable with calculus and linear algebra
- Programming Language: Python (with PyTorch)
- Course Difficulty: 🌟🌟🌟🌟
- Estimated Study Hours: 20

This course is an introductory diffusion model course offered during MIT's IAP term by MIT CSAIL. Taught by MIT students Peter Holderrieth and Ezra Erives, the course provides a clear and accessible explanation of the mathematical foundations of diffusion and flow-matching models from the perspective of differential equations. It also includes hands-on labs where students build diffusion models from scratch, concluding with lectures on applications in cutting-edge areas such as molecular design and robotics.

The accompanying lecture notes are exceptionally well-written and highly recommended for in-depth reading.

## Course Resources

- Course Website: https://diffusion.csail.mit.edu/
- Course Videos: See course website
- Course Textbook: An Introduction to Flow Matching and Diffusion Models
- Course Assignments: Three labs, see course website for details

2025-07-11