CSCI 680: Natural Language Processing
William & Mary · Fall 2026
Instructors
Welcome!
This course provides a comprehensive introduction to natural language processing, spanning foundational techniques through large language models. The first half of the course will focus on linguistic and statistical fundamentals before advancing to neural architectures. The second half of the course will focus on the modern LLM pipeline (e.g. pre-training, post-training, and prompting). Students will also engage with advanced topics and emerging areas in NLP (e.g. interpretability, harms and risks of language modeling, data-centric NLP).
Schedule
| Week | Date | Topic | Course Material |
|---|---|---|---|
| 1 | Wed 08/26 | Course Overview, Linguistic Fundamentals, History of NLP | |
| 1 | Fri 08/28 | Basics of Text Processing | Readings: J&M: §2 |
| 2 | Mon 08/31 | N-Gram Language Models (1) | Readings: J&M: §3 |
| 2 | Wed 09/02 | N-Gram Language Models (2) | Readings: J&M: §3 |
| 2 | Fri 09/04 | Text Classification (1) | Readings: J&M: §4 |
| 3 | Mon 09/07 | No Class - Labor Day | |
| 3 | Wed 09/09 | Text Classification (2) | Readings: J&M: §4 |
| 3 | Fri 09/11 | Word Embeddings (1) | Readings: J&M: §5 |
| 4 | Mon 09/14 | Word Embeddings (2) | Readings: J&M: §5 |
| 4 | Wed 09/16 | Project Pitches | |
| 4 | Fri 09/18 | Project Pitches | |
| 5 | Mon 09/21 | Feedforward Networks | |
| 5 | Wed 09/23 | Backpropagation and RNNs | |
| 5 | Fri 09/25 | RNNs (Cont.) | |
| 6 | Mon 09/28 | Seq2Seq | |
| 6 | Wed 09/30 | Attention | |
| 6 | Fri 10/02 | Language Generation | |
| 7 | Mon 10/05 | Transformers (1) | |
| 7 | Wed 10/07 | Transformers (2) | |
| 7 | Fri 10/09 | No Class - Fall Break | |
| 8 | Mon 10/12 | Hands-On Day | |
| 8 | Wed 10/14 | Transformer LMs (1) | |
| 8 | Fri 10/16 | Transformer LMs (2) | |
| 9 | Mon 10/19 | Pre-Training LLMs | |
| 9 | Wed 10/21 | Guest Lecture: Scaling Laws & Optimization | |
| 9 | Fri 10/23 | Post-Training | |
| 10 | Mon 10/26 | Fine-Tuning, Efficient Adaptation | |
| 10 | Wed 10/28 | Prompting, In-Context Learning, and Chain-of-Thought | |
| 10 | Fri 10/30 | LLM Agents | |
| 11 | Mon 11/02 | Evaluating LLMs | |
| 11 | Wed 11/04 | Responsible Language Modeling | |
| 11 | Fri 11/06 | Paper Discussion 1 | |
| 12 | Mon 11/09 | Project Work Day | |
| 12 | Wed 11/11 | Interpretability | |
| 12 | Fri 11/13 | Paper Discussion 2 | |
| 13 | Mon 11/16 | Robustness & Fairness | |
| 13 | Wed 11/18 | Data-Centric NLP | |
| 13 | Fri 11/20 | Paper Discussion 3 | |
| 14 | Mon 11/23 | Outro | |
| 14 | Wed 11/25 | No Class - Thanksgiving Break | |
| 14 | Fri 11/27 | No Class - Thanksgiving Break | |
| 15 | Mon 11/30 | Final Project Presentations | |
| 15 | Wed 12/02 | Final Project Presentations | |
| 15 | Fri 12/04 | Final Project Presentations |
Deadlines
| Week | Deadline | Released | Due | Time |
|---|---|---|---|---|
| 4 | Project Pitch Slide | — | Mon 09/14 | 11:59 PM |
| 5 | Quiz 1: Statistical Foundations | — | Mon 09/21 | 10:00 AM |
| 5 | Project Team Formation | — | Wed 09/23 | 11:59 PM |
| 5 | Homework 1 | Fri 09/11 | Fri 09/25 | 11:59 PM |
| 6 | Project Proposal | — | Fri 10/02 | 11:59 PM |
| 8 | Quiz 2: Neural Methods | — | Mon 10/12 | 10:00 AM |
| 9 | Homework 2 | Mon 09/28 | Fri 10/23 | 11:59 PM |
| 10 | Quiz 3: LLMs | — | Fri 10/30 | 10:00 AM |
| 11 | Project Progress Report | — | Mon 11/02 | 11:59 PM |
| 13 | Homework 3 | Mon 10/26 | Fri 11/20 | 11:59 PM |
| 14 | Project Final Report | — | Tue 11/24 | 11:59 PM |
Overview
Course Info
- Time: MWF 10:00–10:50 AM
- Location: Integrated Science Center (ISC) 3280
- Office Hours: M 1:00–2:00 PM
Prerequisites
Students should be proficient in Python. Experience with packages such as SciPy, Scikit-learn, and PyTorch is helpful. Students should also have experience with Calculus, Linear Algebra, and Probability & Statistics.
Syllabus
Objectives, grading, policies, and more (PDF)