Description: An undergraduate introduction to foundation models, including representation learning, transformers, large language models, vision and multimodal foundation models, model adaptation, retrieval-augmented generation, agents, evaluation, efficiency, reliability, safety, and responsible use. Students gain practical experience through three assignments, guided research-paper discussions, and a compact team project. Recommended Prerequisite(s): Basic programming experience (e.g., Python), introductory linear algebra, and familiarity with probability or machine learning are recommended but not required.