Covers continuous random variables, probability density functions, and distributions, with practical examples.
Covers the concept of compression and constructing prefix-free codes based on given information.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
Explores extreme events, emission metrics, and the Paris Agreement's goals, analyzing the impact of different metrics on temperature outcomes and the challenges in defining extreme events.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
