Lectures related to Diffusion Models

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Covers the concept of property testing using statistical methods.

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Covers weak solutions in the finite element method, emphasizing continuity and the Cauchy-Schwarz inequality.

Explores Markov chains, transition matrices, distribution, and random walks.

Covers the concept of operators for data transfer and manipulation between activities, including aggregation, distribution, and social operators.

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.