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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. This course is ideal for students who want to develop a principled understanding of the theory and practice of generative AI.

Textbooks:

• Robert Harper, Programming Languages: Theory and Practice
• Benjamin Pierce, TPL

with solutions

syllabus to read Types and Programming Language by Pierce

recommended by someone on Quora as requisite math for learning Machine Learning.

implments popular algorithms in datamining.

a list of courses online

using Python

» MIT OpenCourseWare » Electrical Engineering and Computer Science » Techniques in Artificial Intelligence, Fall 2002