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: Applied text-mining and NLP exercises | 30% |
| Quizzes and Discussions: Concept checks and course discussions | 15% |
| Midterm Exam: Assessment of core NLP concepts | 20% |
| Final Project Report and Code: End-to-end NLP application | 25% |
| Final Presentation: Project presentation and demonstration | 10% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
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.