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MIT 6.S184: Introduction to Flow Matching and Diffusion Models
Computer Science Class 6.S184: Generative AI with Stochastic Differential Equations Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more! This course aims to build up the mathematical framework underlying these models from first principles. At the end of the class, students will have built a toy image diffusion model from scratch, and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic differential equations that is useful in many other fields.
Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more! At the end of the class, students will have built a toy image diffusion model from scratch, and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic differential equations that is useful in many other fields. We would like to thank the following individuals and organizations without whose support this course would not be possible: Professor Tommi Jaakkola without whose support this class would not be possible Lisa Bella, Ellen Reid, and everyone else at MIT EECS for their generous support Christian Fiedler, Tim Griesbach, Benedikt Geiger, and Albrecht Holderrieth for invaluable feedback on the lecture notes Elaine Mello from MIT Open Learning for support with lecture recordings Ashay Athalye from Students for Open and Universal Learning for helping to edit and publish lecture recordings Cameron Diao, Tally Portnoi, Andi Qu, and many others for providing invaluable feedback on the labs The Missing Semester of Your CS Education upon whose website this one was inspired Participants in the original course offering (MIT 6.S184/6.S975, taught over IAP 2025), as well as readers like you for your interest in this course
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