Deep Learning at Scale

Catalog description

This course covers the core architectures and training methods used in contemporary deep learning. Topics include transformers, vision, diffusion, multimodal models (VLM), deep reinforcement learning, mechanistic interpretability and cutting-edge research in areas such as speech and video generation, RAG, MCP and agents, and scientific paper writing. Students will read and critically discuss the seminal papers across deep learning and contribute to that literature. To prepare for this course students are encouraged to have taken CIS 5190 or CIS 5200 or equivalent graduate level Machine Learning background.