AIDA.3203 Probabilistic and Generative Models
Id: 042965 Credits: 3-3Description
This course introduces the statistical foundations of generative modeling, focusing on probability theory, latent variable models, and modern generative AI methods. Students study classical statistical models alongside contemporary approaches such as variational autoencoders, diffusion models, and generative transformers, and conclude the course with a team project. Emphasis is placed on likelihood-based learning, variational inference, sampling, and uncertainty modeling. The course bridges statistical theory and practical generative AI, preparing students to understand, analyze, and build modern generative systems.
Prerequisites
AIDA.2205 Machine Learning, and MATH.2210 Intro to Linear Algebra, and MATH.3850 Applied Statistics.
Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.