AINS6004: Natural Language Processing

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 uses the Aurnova delivery model: GitHub Pages provides learner readings and slides, the restricted instructor repository contains teaching and grading materials, and each enrolled learner receives one complete private student repository for all eight modules.

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?

::{admonition} Your private student workspace :class: tip

This GitHub Pages site is the learner-facing textbook for AINS6004 Natural Language Processing. It intentionally does not link to the restricted instructor repository or to graded exercise files.

For hands-on work, return to the current module in Populi and open the private student repository assigned to you. Clone that repository once, or open its Codespace. To use Colab, choose File → Open notebook → GitHub, authorize your private repositories, and select the lab or exercise from your assigned repository.

Complete the work there, commit and push it, and submit exactly what the Populi assignment requests. Populi remains the official source for due dates, submissions, feedback, and grades.

Start Here#

Choose the path that matches your role:

  • Students: begin with How to Use This Course, review each module’s overview, reading, and slides here, then return to Populi and work in the assigned private student repository.

  • Faculty: begin in the restricted Populi faculty area and instructor repository; teaching notes, grading keys, and reference solutions are intentionally absent from this learner site.

  • Technical setup: use Technical Requirements and Setup. Clone your private student repository once or open its Codespace/Colab notebooks; a paid Colab Pro subscription and dedicated local GPU are not required.

  • Readings: use Authoritative Readings and Resources to connect module claims to primary standards, official documentation, and open textbooks.

  • Student workspace: Populi provides the only authoritative link to each learner’s complete private repository; do not use or request access to a course-source or instructor repository.

What Is Interactive?#

The readings and rendered slides are navigable web pages. Interactive labs and exercises are delivered only through each learner’s private student repository; Populi supplies the official repository link, due date, submission, feedback, and grade. “RISE-ready” means a facilitator can open a slide notebook in JupyterLab for presenter mode; students can read the same deck in the browser without installing RISE.