CS 6785

Deep Probabilistic and Generative Models

Course Description

Course information provided by the Courses of Study 2024-2025.

Generative models are a class of machine learning algorithms that define probability distributions over complex, high-dimensional objects such as images, sequences, and graphs. Recent advances in deep neural networks and optimization algorithms have significantly enhanced the capabilities of these models and renewed research interest in them. This course explores the foundational probabilistic principles of deep generative models, their learning algorithms, and popular model families, which include variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flows. The course also covers applications in domains such as computer vision, natural language processing, and biomedicine, and draws connections to the field of reinforcement learning.

When Offered Spring.

Permission Note Enrollment limited to: Cornell Tech students. 
Prerequisites/Corequisites Prerequisite: CS 2110, MATH 1920, MATH 2940, MATH 4710, or permission of instructor.

• Describe the probabilistic approach to machine learning, including key issues in modeling, inference, and learning of probabilistic models.
• Demonstrate knowledge of modern deep generative machine learning algorithms including variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flows.
• Implement and apply probabilistic and deep generative algorithms to problems and datasets involving images, text, audio, and other modalities.
• Develop an understanding of state-of-the-art results and open research problems in modern deep generative modeling.

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