CCOG for MUC 251 Fall 2024
- Course Number:
- MUC 251
- Course Title:
- Natural Language Processing
- Credit Hours:
- 4
- Lecture Hours:
- 40
- Lecture/Lab Hours:
- 0
- Lab Hours:
- 0
Course Description
Explores foundations of Natural Language Processing (NLP) and text processing in connection with human-machine interaction, human-machine collaboration, and computational poetics. Covers history of computers listening and speaking, coded bias, and appropriation of NLP tools by artists, designers and poets. Prerequisites: MUC 282. Audit available.
Addendum to Course Description
Natural Language Processing can refer specifically to Machine Learning techniques or be taken more broadly to include the fields from which the more specific term originated, such as the history of fields like "computational linguistics." This course intends to leverage the history of NLP as a narrative through which to explore beginner and intermediate coding topics, and explore general mathematics concepts with an interactive, code-forward approach. This course will build confidence through exercises in using code to solve problems, while invoking current themes in AI within a Humanities context.
Intended Outcomes for the course
Upon completion of the course students should be able to:
- Identify use cases and common methods of processing natural language.
- Apply foundational concepts in coding to solve problems.
- Implement effective and/or fun algorithms to retrieve or generate information.
- Assess emergent outcomes of different ways of solving problems within large systems.
- Make connections between computational methods, their proliferation and cultural impact.
Course Activities and Design
- Code Notebooks
- Group/Pair Programming
- Readings and Case Studies
- Interactive Software
- Process Visualizations
Outcome Assessment Strategies
- Code Review
- Class Participation
- Assignments
- Projects
Course Content (Themes, Concepts, Issues and Skills)
- History of computational methods used to process language as it is written or spoken
- Art and Literature that have used computational methods for generation of works
- Information Retrieval
- Machine Translation
- Knowledge Graphs
- Probability