Course Schedule and Catalog
Course Schedule - Spring Semester 2027
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
COMP 484 001 (CRN: 24694)
NATURAL LANGUAGE PROCESSING
Long Title: NATURAL LANGUAGE PROCESSING
Department: Computer Science
Instructor: Chen, Hanjie
Meeting: 4:00PM - 5:15PM MW (11-JAN-2027 - 23-APR-2027)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: COMP 341 OR COMP 441 OR COMP 345
Section Max Enrollment: 30
Section Enrolled: 0
Total Cross-list Max Enrollment: 60
Total Cross-list Enrolled: 0
Enrollment data as of: 2-NOV-2026 11:26AM
Final Exam: No Final Exam
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 Tokenization, Vectorization, Word Embedding, Part-of-Speech Tagging, Named Entity Recognition, Text Classification, Language Modeling, Machine Translation, Question Answering, Deep Learning for NLP, Transformers, Large Language Models, and Advanced Topics in NLP (e.g., Interpretability, Robustness, Fairness).
Students will engage in hands-on projects using popular NLP libraries such as NLTK, SpaCy, and Hugging Face Transformers. They will learn to preprocess text data, implement various NLP algorithms, and evaluate model performance using standard metrics. The course will also include discussions on the ethical implications of NLP, including issues related to bias, privacy, and the societal impact of language technologies. Cross-list: COMP 584. Mutually Exclusive: Cannot register for COMP 484 if student has credit for COMP 584.