CSYE 7380 — Theory and Practical Applications of AI Generative Modeling

4 semester hoursGraduateLectureusually offered: fall, springArlington, VABostonMiami, FLOnlineSeattle, WAOnlineTraditional

Covers transformer-based language models (e.g., ChatGPT and Bard); generative image models (e.g., GAN and variational autoencoder); and generative models for structured data (e.g., Bayesian networks). Explores generative models for data of major modalities, namely, textual, image, and structured relational. Offers students an opportunity to learn how to build such models for practical applications in different verticals using Python and numerous publicly available libraries in Keras/TensorFlow and PyTorch. Given recent surges in generative modeling tools, generative modeling technologies and applications are necessary skills for students entering the field of industrial data science.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 20231114028%29.0
Spring 20241133043%17.0
Fall 202436410064%12.0
Spring 2025610417061%11.0
Fall 2025510315069%9.4
Spring 20263849093%2.0

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 11% · T 1% · W 34% · Th 25% · F 30%

Common patterns: W (34% of sections), F (30% of sections), R (25% of sections), M (11% of sections), T (1% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Percentages are each professor's average share of the season's enrolled students in recent terms.

Links

Official catalog (CSYE course descriptions) · Student reviews on RateMyHusky · All CSYE courses · Plan it at numap.app