Fundamentals of Generative AI

This course provides a foundation in the principles, architectures, and applications of generative artificial intelligence (GenAI). It explores how large language models (LLMs) work by guiding students through the complete model lifecycle: from core building blocks like embedding representations, attention mechanisms, and transformer architectures to adaptation techniques including in-context learning, instruction tuning, parameter-efficient fine-tuning (PEFT), and reinforcement learning from human feedback (RLHF). 

The course also explores advanced topics such as retrieval-augmented generation (RAG), agentic reasoning systems, and multimodal learning that extends beyond text to incorporate vision and other data modalities. 

Programming assignments, research paper readings, and a team-based final project will help students develop the technical proficiency to design, implement, and evaluate GenAI pipelines using Hugging Face, LangChain, and other open-source LLM APIs, while critically assessing their capabilities, limitations, and responsible use in real-world applications.