Course Syllabus

CS275P: Machine Learning with Generative Models

University of California, Irvine, Spring 2024

Prof. Erik Sudderth

CS275P explores methods for statistical machine learning with probabilistic generative models. We will study efficient inference algorithms based on optimization-based variational methods, and simulation-based Monte Carlo methods. Several approaches to learning from data will be covered, including conditional models for discriminative learning, Bayesian methods for controlling model complexity, probabilistic graphical models, and deep generative models such as variational autoencoders and diffusion models.  Methods will be motivated by applications including image and video analysis, text and language processing, sensor networks, autonomous robotics, computational biology, and social networks.

Course Materials

Course Information

Course Prerequisites

A previous introductory course in machine learning (UCI CS273P).  Comfort with multivariable calculus, linear algebra, and probability theory.  Python programming, and some experience with optimization algorithms, required for homeworks and projects.

Course Summary:

Date Details Due