An Introduction to Diffusion and Flow Models - Lecture 1

Explore the foundational concepts of diffusion and flow models in this comprehensive lecture delivered by Dheeraj Nagaraj at the International Centre for Theoretical Sciences. Delve into the mathematical principles underlying these powerful generative modeling techniques that have revolutionized machine learning and artificial intelligence. Learn how diffusion processes work by gradually adding noise to data and then learning to reverse this process to generate new samples. Understand the theoretical framework connecting stochastic differential equations to practical machine learning applications. Examine the relationship between diffusion models and flow-based models, discovering how both approaches enable high-quality data generation across various domains including images, text, and audio. Gain insights into the probabilistic foundations that make these models effective for capturing complex data distributions. Master the key mathematical concepts including forward and reverse diffusion processes, score matching, and the connection to denoising autoencoders. This lecture serves as part of the Data Science: Probabilistic and Optimization Methods II program, providing essential theoretical grounding for understanding modern generative AI systems and their applications in contemporary data science research.