CS 6785

CS 6785

Course information provided by the Courses of Study 2020-2021.

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.

Outcomes
  • 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.

View Enrollment Information

Enrollment Information
Syllabi: none
  •   Regular Academic Session. 

  • 3 Credits Stdnt Opt

  • 19547CS 6785  LAB 430

  • Instruction Mode: Online
    Taught in NYC. Enrollment open to Cornell Tech PhD, and CIS PhD Students based in Ithaca. Enrollment also open to Cornell Tech Master's Students only with instructor permission.