| Description: |
This course is intended for Computer Science students who are interested in understanding the fundamental issues, challenges and techniques that are associated with recent advances in Generative Artificial Intelligence (Generative AI). The course will discuss the history and properties of basic Generative AI systems including foundational probabilistic principles of generative models, their learning algorithms, and several state-of-the-art model families, which include variational autoencoders, generative adversarial networks, autoregressive models, flow based models, energy based models, and diffusion models. The course will be a combination of lectures, discussions, hands-on activities and projects. During the entire course, students will also learn about different applications in domains like computer vision, natural language processing, healthcare, etc. |