Unsupervised Learning

3 Credits · Level: Graduate, Undergraduate

Prerequisite: MATH 5051 and MATH 5151 and MATH 5200 Description: This MS level course covers a range of methods in unsupervised learning with a focus on latent feature inference and learning as well as generative models. We will start with classical methods such as PCA, then consider learning and inference for different statistical models, with an emphasis on connections to clustering and several examples of Bayes nets. In this context we'll cover MCMC and variational approaches. Roughly the last third of the course will cover deep learning methods for unsupervised learning including autoencoders, variational autoencoders and, time permitting, generative diffusions and transformers. Coding in Python and R will be a central component of the course.