ITCS 4101: Introduction to Natural Language Processing
Spring 2026

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Time and Location: Tue, Thu 2:30 – 3:45pm, Duke 207

Instructor & IAs:		Razvan Bunescu		Andrew Morgan		Kento Hopkins
Office:		Woodward 410G		Cone 164		Cone 164
Office hours:		Tue, Thu 4:00 – 5:00pm		Mon 3:00 – 4:00pm		Wed 11:00am – 12:00pm
Email:		rbunescu @ charlotte edu		amorga94 @ charlotte edu		khopki22 @ charlotte edu

Textbook (PDF available online):

Speech and Language Processing (3rd edition draft), by Daniel Juraksfy and James E. Martin; draft released on Jan 6, 2026.

Course description:
Natural Language Processing (NLP) is a branch of Artificial Intelligence whose focus is on the development of computer systems that process or generate natural language. This course will first introduce fundamental linguistic analysis tasks, including tokenization, syntactic parsing, semantic parsing, and coreference resolution. We will then study vector based representations of text, ranging from bag-of-words and TF-IDF to neural word and text embeddings. The course will survey machine learning models and techniques underlying modern NLP, including attention and Transformer-based language models, which will be used in a number of NLP applications such as sentiment classification, information extraction, or question answering. In parallel, the course will introduce standard frameworks for developing workflows where LLM-based agents connect with tools and communicate with other agents. Overall, the aim of this course is to equip students with an array techniques and tools that they can use to solve known NLP tasks, as well as new types of NLP problems.

Prerequisites:
Introduction to Machine Learning (ITCS 3156). Students are expected to be comfortable with programming in Python, data structures and algorithms (ITSC 2214), and basic machine learning techniques. Relevant background material will be made available on this website throughout the course.

Lecture notes:

1. Syllabus & Introduction
2. Python for programming, linear algebra, and visualization
   • Python lecture
   • Python tutorial
   • NumPy tutorial
   • Matplotlib tutorial
3. Tokenization: From text to sentences and tokens
   • Chapter 2, sections on words, morphemes, rule-based tokenization, BPE, and corpora.
4. Regular expressions
   • Regular expressions in Python with re.
   • Regular expressions in UNIX with sed.
5. Text classification using Logistic Regression
   • Chapter 4 on logistic regression and text classification.
6. LLMs: use scenarios, strengths and weaknesses
   • Chapter 7, sections on architectures, generation, and sampling.
7. LLMs: application development through APIs
8. LLMs: connecting applications with tools and external resources
9. LLMs: Efficient Factual grounding using RAG and vector DBs
10. LLMs: Connecting AI applications to external resources using MCP
11. LLMs: developing and deploying multi-agent systems
    • Agentic AI course by Andrew Ng @ DeepLearning.AI.
12. Word meanings; Sparse vs. dense representations of words
    • Chapter 5 on vector semantics and embeddings from the textbook.
13. N-grams and Neural models for Language Modeling and Sequence Processing
    • Chapter 3 on n-gram language models from the textbook.
    • Chapter 6 on neural networks from the textbook.
    • Chapter 13, up to 13.6 on RNNs and LSTMs from the textbook.
    • All Our N-gram are Belong to You
14. Machine translation, Sequence-to-sequence models and Attention
    • Chapter 12 on machine translation from the textbook.
    • Chapter 13.7 and 13.8 on encoder-decoder models with attention from the textbook.
15. Transformers and Self-Attention
    • Chapter 8 in J & M on Transformer.
16. Biases vs. fairness and rationality in NLP models
    • Section 7.7 in J & M on Ethical and Safety Issues with Language Models

Homework assignments¹:

• Assignment 0 on Python lists and strings.
  • Skeleton code.
• Assignment 1 on Word distributions.
  • Skeleton code and data.
• Assignment 2 on Wikipedia processing with regular expressions.
  • Skeleton code and data.
• Assignment 3 on Sentiment Analysis with Logistic Regression and engineered features.
  • Skeleton code and data.
• Assignment 4 on Question Answering on semi-structured restaurant data using the chat completion API with GPT, LLama, or Gemini.
  • Skeleton code and data.
• Assignment 5 on Restaurant Chatbot by interfacing LLMs with external tools.
  • Skeleton code and data.
• Assignment 6 on MCP client-server architecture for Restaurant Chatbot.
  • Skeleton code and data.
• Assignment 7 on Agentic Workflows for research, using planning and tools.
  • Skeleton code and data.
• Assignment 8 on Vector Representations of Words.
  • Skeleton code and data.
• Assignment 9 on RNNs for Sentiment Classification.
  • Skeleton code and data.
  • Educational cluster instructions.
• Assignment 10 on Transformer-based models for NLP (optional).
  • Skeleton code and data.

Background reading materials:

• Python programming:
  • Google's Crash Course on Python
  • W3 Schools' Python tutorial
  • Python for Everybody
• Mathematics for Computer Science textbook on zyBooks.
• Probability and statistics:
  • Basic probability theory (pp. 12-19) in Pattern Recognition and Machine Learning.
  • Chapter 3 in DL textbook on Probability and Information Theory.
  • Parts 1 and 2 in CS109: Probability for Computer Scientists course reader.
  • Seeing Theory: A visual introduction to probability and statistics, Kunin et al., 2018.
• Linear Algebra:
  • Chapter 2 in DL textbook on Linear Algebra.
  • Interactive Linear Algebra, Margalit and Rabinoff, Georgia Tech, 2017.
  • Chapter 2 on Linear Algebra in Mathematics for Machine Learning.
  • Inderjit Dhillon's Linear Algebra Background
  • Gilbert Strang's Introduction to Linear Algebra
  • Petersen et al.'s The Matrix Cookbook
  • Mike Brookes' Matrix Reference Manual
• Calculus:
  • Basic properties for derivatives, integrals, exponentials, and logarithms.
  • Chapter 4.3 in DL textbook on Numerical Computation.
  • Gilbert Strang's Calculus texbook.

Supplemental readings:

• Accurate methods for the statistics of surprise and coincidence, Ted Dunning, Computational Linguistics, 1993.
• Attention is all you need, Vaswani et al., NeurIPS 2017.
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL 2019.
• Durably reducing conspiracy beliefs through dialogues with AI, Costello et al., Science, September 2024.
• AI can help humans find common ground in democratic deliberation, Tessler et al., Science, October 2024.

Tools and packages:

• Natural language processing:
  • spaCy
  • brat rapid annotation tool
  • FAISS open-source library for AI similarity search.
  • LangExtract for LLM-powered information extraction.
• Machine learning:
  • PyTorch Deep learning in Python
  • PyTorch tutorial from University of Amsterdam.
  • Scikit-learn Machine learning in Python
  • Hugging Face ML models and datasets