Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Covers the concept of compression and constructing prefix-free codes based on given information.
Covers advanced numerical analysis topics including deep neural networks and optimization methods.
