About this course
Natural Language Processing (NLP) is the subfield of Artificial Intelligence that deals with tasks involving human languages. Recently, “Large Language Models (LLMs),” (e.g. ChatGPT) have advanced the field and generated great excitement.
This course is an introduction to NLP which focuses on understanding those Large Language Models. We will discuss and implement the various algorithms needed to create an LLM, including Tokenization, Vectorization, Basic Neural Networks, Transformers and Attention Modules, and Fine Tuning a Pretrained LLM model. By the end of the course, the students will have built a complete “small” LLM and will have experimented with it. Students will code in Python using various libraries.
Syllabus
Summer 2026 (Draft)
Go to syllabusThis is a draft syllabus. The final syllabus will be available here a few days prior to the new course’s first start date.
Pre-requisites
One year of computer science at university level. A course in data structures or a course in algorithms. Knowledge of a programming language (e.g. in Python/Javascript/Java/C++).
Faculty
John Rager
FacultyProf. Rager earned his Ph.D. from Northwestern University. He is a full professor at Amherst College in Amherst, Massachusetts where he has taught since 1988. He has always been interested in languages, both human and computer. His dissertation was in the field of symbolic natural language processing and subsequent to that his research has shifted to (among other things) natural language processing using machine learning. He has also worked on applying Artificial Intelligence to teaching English to Speakers of Other Languages. This work was motivated by the difficulties faced by English teachers in Moldova, where he was a Fulbright Scholar during the 2003-04 academic year. His teaching has often touched on language. For example, he has taught a seminar for first-year students called “Natural and Unnatural Languages.” The material in that course included “traditional” natural language processing as done in artificial intelligence, but also a discussion of rhetorical devices in Shakespeare, a reading of parts of Finnegan’s Wake and a discussion of language evolution. He has also taught a course on Digital Textual Analysis. That course discussed the computer science (e.g. topic modeling, Naive Bayes classification) used in papers in digital humanities. The course included both Computer Science and Humanities students, who worked together in groups on projects.