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