Schedule¶
CS-5293 Natural Language Processing — Spring 2026 (Jan 19 – May 8, 2026)
Tentative schedule
Lectures meet Tuesday & Thursday, 1:30–2:45 pm in Sarkeys Energy Center A0236. Dates, topics, and lecture grouping may shift during the semester; the authoritative deadlines are always those posted on Canvas. Assignments are due at 12:00 pm (noon) on the listed date.
Weekly schedule¶
| Week | Tue / Thu | Lecture | Topic | Reading (J&M 3rd ed.) | Deadlines |
|---|---|---|---|---|---|
| 1 | Tue Jan 20 | Lec 01 | Course Introduction | Ch. 1 | |
| Thu Jan 22 | Lec 02 | Tokens, Words, Vocabularies, Lexicons | Ch. 2 | ||
| 2 | Tue Jan 27 | Lec 03 | Language Modeling with N-Grams | Ch. 3 | HW1 released |
| Thu Jan 29 | Lec 04 | N-Gram Generation & Smoothing | Ch. 3 | ||
| 3 | Tue Feb 3 | Lec 05 | Text Classification | Ch. 4 | |
| Thu Feb 5 | Lec 06 | Logistic Regression & Feature Engineering | Ch. 5 | ||
| 4 | Tue Feb 10 | Lec 07 | Wrap-up: Text Classification | Ch. 4–5 | HW1 due (Feb 10) |
| Thu Feb 12 | Lec 08 | Token Classification & Sequence Labeling | Ch. 8 | Project Milestone 1: Proposal | |
| 5 | Tue Feb 17 | Lec 09 | Structured Prediction: the Viterbi Algorithm | Ch. 8 | HW2 released |
| Thu Feb 19 | Lec 10 | Vector Semantics & Embeddings | Ch. 6 | ||
| 6 | Tue Feb 24 | Lec 11 | Vector Semantics → Neural Networks | Ch. 6–7 | |
| Thu Feb 26 | Lec 12 | Neural Networks for NLP | Ch. 7 | HW2 due (Feb 26) | |
| 7 | Tue Mar 3 | Lec 13 | Recurrent Neural Networks & LSTMs | Ch. 9 | HW3 released |
| Thu Mar 5 | Lec 14 | Machine Translation: Seq2Seq & Attention | Ch. 10, 13 | ||
| 8 | Tue Mar 10 | Lec 15 | Attention | Ch. 9–10 | |
| Thu Mar 12 | Lec 16 | The Transformer | Ch. 9 | ||
| 9 | Tue Mar 17 | — | Spring Break — no class | ||
| Thu Mar 19 | — | Spring Break — no class | |||
| 10 | Tue Mar 24 | Lec 17 | Transformer Recap | Ch. 9 | HW3 due (Mar 24) |
| Thu Mar 26 | Lec 18 | Pretrained Transformers (BERT, MLM) | Ch. 11 | Project Milestone 2: Progress report & mid-term demo | |
| 11 | Tue Mar 31 | Lec 19 | T5 & Encoder–Decoder LLMs | Ch. 11–12 | HW4 released |
| Thu Apr 2 | Lec 20 | LLMs & In-Context Learning | Ch. 12 | ||
| 12 | Tue Apr 7 | Lec 21 | In-Context Learning & Prompting (cont.) | Ch. 12 | |
| Thu Apr 9 | Lec 22 | Prompt-Based Tuning & PEFT (LoRA/QLoRA) | Ch. 12 | HW4 due (Apr 9) | |
| 13 | Tue Apr 14 | Lec 23 | Post-Training Overview | notes | HW5 released |
| Thu Apr 16 | Lec 24 | Post-Training: Supervised Fine-Tuning (SFT) | notes | ||
| 14 | Tue Apr 21 | Lec 25 | Post-Training: Reinforcement Learning (RLHF) | notes | |
| Thu Apr 23 | Lec 26 | Test-Time Inference & Reasoning | notes | HW5 due (Apr 23) | |
| 15 | Tue Apr 28 | Lec 27 | Agentic AI & Applications | notes | |
| Thu Apr 30 | — | Final Project Presentations I | Project Milestone 3: Final report due | ||
| 16 | Tue May 5 | — | Final Project Presentations II | ||
| Thu May 7 | — | No class | |||
| Finals | May 11–15 | — | No final exam |
Assignment due dates at a glance¶
| Assignment | Topic | Released | Due (12:00 pm) |
|---|---|---|---|
| HW1 | Tokenization & N-Gram Language Models | Week 2 (Jan 27) | Tue Feb 10 |
| HW2 | Machine Learning & Text Classification | Week 5 (Feb 17) | Thu Feb 26 |
| HW3 | Neural Text Classification & Structured Prediction | Week 7 (Mar 3) | Tue Mar 24 |
| HW4 | Neural & Pretrained Models (RNN/LSTM, Transformer, BERT, T5) | Week 11 (Mar 31) | Thu Apr 9 |
| HW5 | LLM Post-Training & Agentic AI | Week 13 (Apr 14) | Thu Apr 23 |
Project milestones¶
| Milestone | What | When |
|---|---|---|
| 1 — Proposal | Initial project proposal (shared task or your own) | Week 4 (Feb 12) |
| 2 — Progress | Progress report + 10-min mid-term demo (office hours) | Week 10 (Mar 26) |
| 3 — Final | Final report due; in-class final presentations | Week 15 (Apr 30) |
Slides & notebooks
Lecture slides are posted on Canvas. The hands-on notebook chapters for each assignment are linked from the Table of Contents and can be opened in Colab with one click.