THE STATISTICAL FOUNDATION OF GENERATIVE ARTIFICIAL INTELLIGENCE
This course is an advanced machine learning course that focuses on the statistical foundations of generative AI in the Data Science (DSCI) Minor Program. In this course, students will study the fundamental concepts of statistics and understand the evolution of machine learning from discriminative to generative models. Specifically, students will learn two main foundation models: Transformers and diffusion models, via understanding their statistical foundations, comparing their advantages to classical models, and implementing the two models in real-world applications. Cross-list: DSCI 420 . Graduate/Undergraduate Equivalency: STAT 626 .
Prerequisites & course information
Short Title: STAT FOUNDATION GENERATIVE AI Department: Statistics Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): ( BIOE 439 or BUSI 395 or DSCI 301 or STAT 315 or ELEC 303 or PSYC 339 or SOCI 382 or SOSC 302 or STAT 280 or STAT 305 or STAT 310 or ECON 307 or STAT 311 ) and ( CMOR 438 or COMP 341 or COMP 540 or DSCI 303 or ELEC 378 or ELEC 478 or STAT 413 or COMP 330 or DSCI 302 )
Sources
- Course catalogue ↗2026–2027