CS 639, Spring 2026
Department of Computer Sciences
University of Wisconsin–Madison
Large pretrained machine learning models, also known as foundation models, have taken the world by storm. Models like the GPT family, Claude, and Gemini have astonishing abilities to answer questions, speak with users, and generate sophisticated content. This course covers all aspects of these fascinating models. We will start with a broad review/introduction to modern neural networks and artificial intelligence. We will then learn how foundation models are built, including model architectures, pre-training, post-training, and adaptation. Next, a significant focus is how to use and deploy foundation models, including prompting strategies, providing in-context examples, fine-tuning, integrating into existing data science pipelines, and more. We discuss recent advances in applying foundation and large language models, including reasoning, agents, and systems built on top of LLMs. Finally, we cover the potential societal impacts of these models.
We assume students have familiarity with basic machine learning. The prerequisites are: