Advances in Foundation Models

Undergraduate

​​Foundation models are a recent class of AI models that are large-scale in terms of number of parameters and are trained on broad data (generally using self-supervision at scale). These models have demonstrated exceptional capabilities in natural language processing, computer vision, and other tasks. In this course, we explore advanced topics in modern AI, focusing on the principles, methods, and emerging directions underlying contemporary language models and systems. It emphasizes a deeper understanding of how such systems are developed, adapted, evaluated, and deployed, while situating current methods within the broader trajectory of natural language processing and machine learning. The course is designed to remain responsive to a fast-moving field. While its specific emphasis may evolve over time, it consistently addresses core questions about model design, capabilities, interpretability, reasoning, evaluation, reliability, and impact. The course primarily focuses on language models and natural language processing as the main domain, while covering select topics in computer vision and multi-modality. Prerequisite: Either CPSC 4770 , CPSC 4520 , or CPSC 4800 , CPSC 4830 , or permission of the instructor. MW 2:35pm-3:50pm