Courses

(Prereq. DSC 255R)

This course covers advanced techniques for unsupervised machine learning, including dimensionality reduction, descriptive statistics; clustering; projection, singular value decomposition, and spectral embedding; common probability distributions; density estimation; graphical models and latent variable modeling; sparse coding and dictionary learning; autoencoders, shallow and deep; and self-supervised learning.