Syllabus: AINS6004 Natural Language Processing

Catalog Description

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

Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

Weekly Schedule

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

Assessment

Component	Weight
Weekly labs and notebooks	30%
Applied assignments	35%
Participation and technical critique	15%
Final synthesis portfolio	20%

Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.