CS 5788
Introduction to Generative Models
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.
Topics
- 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.
Official roster learning outcomes
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Introduction to Generative Models · 3.0 · Graduate