Course Description
Students collect and analyze unstructured text data using web/API scraping methods, and then analyze their corpus using text mining and natural language processing. Additionally, students conduct a survey of relevant issues pertaining to privacy rights and intellectual property rights for web scraping and text mining methods.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
Explain foundational concepts and applications of text mining and NLP.
Collect, clean, tokenize, normalize, and linguistically annotate text data.
Represent text using Bag-of-Words, TF-IDF, n-grams, and embeddings.
Build and evaluate machine-learning models for text classification and sentiment analysis.
Apply topic modeling, clustering, named entity recognition, and information extraction.
Explain neural sequence models, attention, transformers, and pretrained language models.
Use Generative AI and LLM tools for prompting, semantic search, and basic RAG applications.
Evaluate NLP systems for accuracy, bias, privacy, hallucination, and responsible use.
Course Grading Information:
Activity/Performance Measure | Percentage/Points |
|---|---|
Programming Assignments | 30% |
Quizzes and Discussions | 15% |
Midterm Exam | 20% |
Final Project Report and Code | 25% |
Final Presentation | 10% |
Attendance/Participation:
Students are expected to complete weekly Canvas modules, readings, videos, discussions, quizzes, and assignments. Regular participation is expected.
Work submitted within seven calendar days after the deadline may receive a 25% deduction. Work more than seven days late is normally not accepted. Documented emergencies should be communicated promptly.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.