Topics in Data Science Methodologies

Spring 2026 Topic: Theory and Applications of Large Language Models. In this course, students will become savvy consumers and sophisticated developers of LLMs and related tooling. We will start by orienting ourselves to the history of natural language processing and the current state of AI tools, which students will learn to critically evaluate and use extensively throughout the course. Students will develop a deep intuition for LLM concepts including attention and transformer architectures, sampling, and search, and will build small models from scratch. They will then apply pre-trained LLMs to solve real-world problems, working with advanced techniques including fine-tuning, prompt engineering, RAG, and AI agents. Throughout, the course emphasizes bias, safety, and responsible deployment. Through reflections, labs, and projects, students will demonstrate learning and develop a professional portfolio.