CS 5788

Introduction to Generative Models

Course Description

Course information provided by the 2025-2026 Catalog.

An in-depth introduction to deep generative models. This course covers the mathematical foundations of generative models and their implementation as deep neural networks. Topics include diffusion models, variational autoencoders, generative adversarial networks, and network architectures for generation. These topics will be discussed in the context of applications in computer vision and natural language processing.

Enrollment Priority Enrollment limited to: Cornell Tech students.

Last 4 Terms Offered (None)

Learning Outcomes

• Understand the mathematical foundations behind deep generative models, including autoregressive models, diffusion models, and variational autoencoders.| Implement generative models and use them to solve problems in computer vision, natural language processing, and speech and audio processing.
• Apply generative models to representation learning and for transferring knowledge between tasks.
• Develop an understanding of the real-world challenges of training and deploying generative models.

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