Place and Time: HOA 160, TR 3:30-4:50P
Instructor: Eric Nyberg
Course Description: This course is designed to be accessible to Masters and advanced undergraduate students who seek the basic skills necessary to implement practical Natural Language Processing (NLP) applications using Language Models (LMs) in specific information domains. The syllabus includes learning materials on the core concepts of NLP and LMs, and how they are applied in closed commercial systems (e.g. ChatGPT) as well as open systems (e.g. Llama, T5). Students complete a set of hands-on exercises in Python that develop skills in applying NLP for various practical problems.
Textbook: Jurafsky and Martin, "Speech and Language Processing"
Prerequisite Knowledge: Strong programming skills (in Python); A course in data structures and algorithms (or equivalent experience); A basic knowledge of probability theory and linear algebra
Course Goals: Students acquire basic knowledge of NLP approaches, including language representations, probability theory and language modeling, logistic and softmax regression, word embeddings, neural networks and large language models; and NLP tasks, such as document classification, parsing, knowledge representation and reasoning, translation, and question answering.
Grading (F'25, subject to revision):
Syllabus (S'25, subject to revision):
Last Updated January 12, 2026