Course Description 

Natural language processing (NLP) is the study of human language from a computational perspective. This course is an introductory graduate-level course on natural language processing aimed at students who are interested in doing cutting-edge research in the field. In this class, we will cover recent developments on core techniques and modern advances in NLP, especially large language models and language grounding. We will also survey some recent NLP research topics including multimodality, interactivity, and interoperability for NLP. 

Students will gain the necessary skills and experience to understand, design, implement, and test large language models through a final project. We will also introduce cutting-edge research topics and learn how to conduct NLP research through paper readings and discussions. We will potentially also host invited speakers for talks. 

Prerequisites 

We require students to have prior knowledge undergraduate linear algebra, probability and statistics, machine learning, or deep learning. Introduction to natural language processing is recommended.