Natural Language Processing
(CSCI-SHU 376)
Fall 2026 | New York University Shanghai
Instructor
Hua Shen
Time
Mon/Wed 11:15 AM - 12:30 PM
Location
S311
Welcome! 🤗
Natural language processing (NLP), a form of artificial intelligence (AI) that gives computers the ability to read, understand and interpret human languages, is one of the most important technologies that have made significant progress recently. NLP has been applied to many areas such as machine translation, question answering, summarization, dialogue etc. The course will introduce students to the basics of NLP, including standard frameworks as well as algorithms and techniques to solve NLP problems, including recent deep learning approaches.
Overview
Office Hour
Hua Shen: 2:30 PM - 4:30 PM, Friday (or by appointment);
Office: S749; Zoom: https://nyu.zoom.us/my/hua.shen (Passcode: enQb9h)
Office: S749; Zoom: https://nyu.zoom.us/my/hua.shen (Passcode: enQb9h)
Prerequisite:
Required: Machine Learning, Calculus, Probability and Statistics;
Desirable/Helpful: Linear Algebra, Data Structures, Pytorch
Desirable/Helpful: Linear Algebra, Data Structures, Pytorch
Class Schedule
See NYU Shanghai's Course Syllabus for the tentative schedule, which is subject to change.
| Date | Theme | Topics | Reading Materials | |
|---|---|---|---|---|
|
1
|
Aug 31 (Mon) |
Introduction: NLP Landscape and History, Course Objectives
Lecture 1
Team Registration
|
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|
1
|
Sep 2 (Wed) | Theme 1: Statistical and Feature-Based NLP |
N-gram LMs
Lecture 2
|
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2
|
Sep 7 (Mon) | Theme 1: Statistical and Feature-Based NLP |
Text classification & log-linear models
|
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2
|
Sep 9 (Wed) | Theme 2: Neural NLP |
Word Embeddings, Pytorch
🎓 Quiz 1
🎓 HW1 Out
|
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|
3
|
Sep 14 (Mon) | Theme 2: Neural NLP |
Neural Networks
|
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|
3
|
Sep 16 (Wed) | Theme 2: Neural NLP |
RNN & LSTM
🎓 Quiz 2
🎓 Project Proposal Due
|
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|
4
|
Sep 21 (Mon) | Theme 2: Neural NLP |
Machine Translation (Seq2Seq Models)
|
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|
4
|
Sep 23 (Wed) | Theme 3: Transformers & Pretraining |
Transformers
🎓 Quiz 3
|
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5
|
Sep 28 (Mon) | Theme 3: Transformers & Pretraining |
Contextualized representations and LM-pretraining
|
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5
|
Sep 30 (Wed) | Theme 4: Foundation Models / LLMs |
LLM Pretraining and Instruction Finetuning
🎓 Quiz 4
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6
|
Oct 12 (Mon) | Theme 4: Foundation Models / LLMs |
LLM in-context Learning and RLHF
🎓 HW1 Due
|
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6
|
Oct 14 (Wed) | Theme 4: Foundation Models / LLMs |
LLM Decoding
🎓 HW2 Out
|
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|
7
|
Oct 19 (Mon) |
NO CLASS, Mid-Term Preparation
|
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|
7
|
Oct 21 (Wed) |
NO CLASS, Mid-Term Preparation
|
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|
8
|
Oct 26 (Mon) | MIDTERM | 🎓 Midterm Exam | |
|
8
|
Oct 28 (Wed) |
NO CLASS, Meet Hua about Project Progress
|
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|
9
|
Nov 2 (Mon) | Theme 5: Agentic LLMs |
LLM Agents
🎓 Quiz 5
|
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9
|
Nov 4 (Wed) | Theme 5: Agentic LLMs |
Multi-Agent Systems
🎓 HW2 Due
🎓 HW3 Out
|
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10
|
Nov 9 (Mon) |
🎓 Mid-Project Presentation I |
||
|
10
|
Nov 11 (Wed) |
🎓 Mid-Project Presentation II |
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|
11
|
Nov 16 (Mon) |
NO CLASS, Meet Hua about Final Projects
|
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|
11
|
Nov 18 (Wed) | Theme 6: Open Research Directions of LLMs |
Value Alignment of LLM
🎓 Quiz 6
|
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|
12
|
Nov 23 (Mon) | Theme 6: Open Research Directions of LLMs |
Retrieval-augmented LLM
|
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12
|
Nov 25 (Wed) | Theme 6: Open Research Directions of LLMs |
LLM Reasoning
🎓 HW3 Due
|
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13
|
Nov 30 (Mon) | Theme 7: LLM Interpretability |
Mechanistic Interpretability Approaches
|
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13
|
Dec 2 (Wed) | Theme 7: LLM Interpretability |
Human Evaluation and Interactive Explanation
|
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|
14
|
Dec 7 (Mon) | Theme 8: Speech Processing |
Speech Language Models
🎓 Quiz 7
|
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|
14
|
Dec 9 (Wed) |
NO CLASS, Meet Hua about Final Projects
🎓 Project Due
|
||
|
15
|
Dec 14 (Mon) |
🎓 Final-Project Presentation I |
||
|
15
|
Dec 16 (Wed) |
🎓 Final-Project Presentation II |