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Abstract: This is a graduate-level research course on Transformer and Large Language Models. Over the duration of this course, we will delve into the latest publications within the expansive domain of Large Language Models (LLMs), intelligent agents, and multimodal learning. Each student is entrusted with the following responsibilities: Conducting critical analyses and authoring paper reviews, programming with LLMs, delivering comprehensive paper presentations, and undertaking a substantial, research-quality course project. The intent behind this course is to foster a deep understanding of LLMs and their pivotal role in today's technological landscape.

Each student is expected to read papers before lecture, write paper reviews or program with LLMs, present papers,  and complete a research-quality course project (e.g., implement an existing algorithm or solve a new problem creatively using deep learning. One team could have two students. ).  Projects that simply apply Transformers/LLMs are not encouraged.

Prerequisites: Neural network building experience or successfully finished an introductory deep learning course.