1. Course Information

• Instructor: Hanjie Chen

• Semester: Spring 2025

• Time: Monday and Wednesday, 4:00PM - 5:15PM

• Location: GRB W212

• Email: hanjie@rice.edu

• TA: Chunyuan Deng

• Office Hour: 

Hanjie: Friday 3:30PM - 4:30PM, DH 2081

Chunyuan: Friday 1:00PM - 2:00PM, Zoom

2. Course Description

This course provides an in-depth introduction to Natural Language Processing (NLP), a field at the intersection of computer science, artificial intelligence, and linguistics that focuses on the interaction between computers and human language. As NLP technologies continue to transform industries—from chatbots to virtual assistants—this course equips students with the foundational knowledge and practical skills necessary to understand and develop NLP applications.

Throughout the semester, students will explore key concepts and methodologies in NLP, such as text preprocessing, language modeling, and deep learning approaches. This course will cover a wide range of NLP topics, including Text Classification, Word Embeddings, Language Modeling, Machine Translation, Natural Language Generation, Transformers, Pre-trained and Large Language Models, Modern Techniques (Prompting, In-context Learning, RLHF, etc.), and Advanced Topics in NLP (e.g., Interpretability, Robustness, Fairness).

3. Assignments and Evaluation

• Quizzes: 8 * 5% = 40%

From Week 3 to Week 13, there will be 8 quizzes (please refer to the Schedule). Each quiz will take 15 minutes at the beginning of class and is closed-book. The format may include question answering or small coding tasks.

• Project: 60%

  • Project proposal: 20%

    • Introduction (5%)

    • Problem Statement and Objective (5%)

    • Method (5%)

    • Plan (5%)

  • Final presentation: 40%

    • Introduction (5%)

    • Problem Statement and Objective (5%)

    • Method (10%)

    • Experiments (15%)

    • Conclusion, including the contributions of each team member (5%)

There is one course project, and 2-3 students can form a team. Each group will give a proposal (7-8 minutes) in the middle of the semester and give a final presentation (7-8 minutes) at the end of the semester. The project should focus on NLP tasks or applications. Possible projects include: Machine Translation for Low-Resource Languages, Text Summarization for Long Documents...

While many Large Language Models (LLMs) such as GPT-4 can solve a wide range of NLP tasks, students are encouraged to experiment with and compare the performance of traditional NLP models (e.g., LSTM) as part of the project. It is acceptable to use LLMs for your project, but students should not rely solely on API-based models for their experiments.

Upload code and slides to Canvas.

• Grading

The letter grade will be assigned based on the points accumulated:
A+ (96-100), A (90-95), A- (87-89), B+ (84-86), B (80-83), B- (77-79), C+ (74-76), C (70-73), C- (67-69), D+ (64-66), D (60-63), F (<60)

4. Prerequisites

Students are expected to have completed at least one machine learning course such as COMP 341 (Practical Machine Learning), COMP 441 (Large-Scale Machine Learning), and COMP 642 (Machine Learning). They should know the foundations of machine learning, such as logistic regression, cross validation, optimization with gradient descent, bias and variance decomposition, etc.

Students should be proficient in Python, with some additional packages like SciPy, Scikit-learn, and PyTorch. Students are also expected to have backgrounds in Calculus and Linear Algebra, Probability and Statistics, such as mean and variance, multinomial distribution, conditional dependence, maximum likelihood estimation, Bayes theorem, etc.

5. Honor Code

All students are expected to adhere to the standards of the Rice Honor Code, which you agreed to uphold upon matriculation. For detailed information on the Honor Code, including its administration and the procedures for addressing alleged violations, please refer to the Honor System Handbook available at http://honor.rice.edu/honor-system-handbook/. This handbook outlines the University’s expectations for academic integrity, the procedures for resolving any alleged violations, and the rights and responsibilities of students and faculty throughout the process.

Note that students should use AI tools carefully in their assignments. Please follow the university's AI Usage Guidelines.