This is Part 2 of a two-part graduate sequence in deep learning. Building on the foundations from Part 1, it focuses on advanced generative modeling.
You will study autoregressive models, diffusion models, energy-based models, and normalizing flows; see how these techniques converge in multimodal text-to-image systems such as CLIP, DALL-E 2, Imagen, and Stable Diffusion; and apply generative methods to creative domains such as music generation. The course concludes by synthesizing the full arc—from discriminative foundations to advanced generative AI—and examining the ethical and societal implications of deploying these systems.
