Deep Learning for Computer Graphics COMSBC3997 Spring 2024


This course covers neural network architectures (deep learning), related math and the fundamental theory and application of AI algorithms most popular in the field of computer graphics. Programming assignments will help students develop graphics processing unit (GPU) programming skills while implementing concepts learned in lectures and readings using deep learning APIs on a GPU cluster. Generative Adversarial Networks (GANs) for facial animation is a project example.


This course covers the fundamental theory and application of AI algorithms in the context of computer graphics. The course begins with deep learning basics including related math review (numerical analysis and gradient optimization). Building upon this foundation, students learn essential deep learning concepts including: supervised, unsupervised and reinforcement learning, and operations relevant to neural network architectures (like backpropagation and fine tuning). There is an emphasis on developing GPU programming skills while implementing real computer graphics applications using learned concepts. Two homework assignments, weekly quizzes and one take-home exam compliment these programming assignments and help students evaluate their comprehension of course material. The course culminates with student-designed final projects that demonstrate creativity, and a depth of knowledge in two or more course topics. Convolutional neural networks for colorizing black and white movies, transformers for image classification, and generative adversarial networks for stylized rendering are application examples.


Instructor: Dr. Corey Toler-Franklin, Computer Science Department, Barnard College, Columbia University

Contact: Office Milstein 502B,

Office Hours: TBD



Prerequisites: COMS W3157 Advanced Programming, Linear Algebra (UN2010), and Calculus I or higher.

**Contact instructor if you are not sure you are prepared for the course**

Syllabus and more information coming soon.