# AINS6004: Natural Language Processing

**Aurnova MSAI track:** Core  
**Credits:** 3  
**Format:** 8-week online graduate course

Builds NLP systems spanning text processing, embeddings, transformers, RAG, conversation, and evaluation.

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in Natural Language Processing;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

1. **Text preprocessing and linguistic signals** — What is lost and gained when language becomes data?
2. **Embeddings and semantic similarity** — How do vector representations support semantic operations?
3. **Language modeling foundations** — What does a language model learn from context?
4. **Transformers for NLP tasks** — How are transformer encoders and decoders adapted to applications?
5. **Retrieval-augmented generation** — How does retrieval change reliability and accountability?
6. **Conversation design and tool use** — How do dialogue systems coordinate context, tools, and user intent?
7. **Evaluation for NLP systems** — Why are output quality and factuality hard to measure?
8. **NLP system deployment review** — What makes a language system ready for use?