Introduction to Natural Language Processing

Introductory course in natural language processing, covering core computational problems involving human language, and both classical techniques such as bag-of-words, n-grams and and conditional random fields, as well as modern neural approaches such as word vectors, recurrent neural networks and transformers. We will also lay the foundation for understanding large language models and their scaling properties. Focus will be on key NLP tasks such as text classification, language modeling, machine translation and information retrieval. Prerequisite: COMPSCI 201; and one of COMPSCI 270, 370, or 371. Must be comfortable with Python programming.