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20 lines
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20 lines
1.2 KiB
Markdown
# MIT6.S184: Generative AI with Stochastic Differential Equations
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## Course Introduction
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- University: MIT
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- Prerequisites: Basic understanding of deep learning, and be comfortable with calculus and linear algebra
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- Programming Language: Python (with PyTorch)
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- Course Difficulty: 🌟🌟🌟🌟
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- Estimated Study Hours: 20
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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.
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The accompanying lecture notes are exceptionally well-written and highly recommended for in-depth reading.
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## Course Resources
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- Course Website: https://diffusion.csail.mit.edu/
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- Course Videos: See course website
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- Course Textbook: [An Introduction to Flow Matching and Diffusion Models](https://arxiv.org/abs/2506.02070)
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- Course Assignments: Three labs, see course website for details
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