AINS6002: Machine Learning & Predictive Modeling

AINS6002: Machine Learning & Predictive Modeling#

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

Covers supervised, unsupervised, and operational predictive modeling with reproducible 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 Machine Learning & Predictive Modeling;

  • 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. Prediction tasks and data framing — What decision is the model actually supporting?

  2. Data preparation and feature pipelines — How do preprocessing choices shape model behavior?

  3. Linear and tree-based baselines — Why do strong baselines matter before complex models?

  4. Model selection and validation — How do we choose models without overfitting our evidence?

  5. Unsupervised learning and structure discovery — How can models reveal patterns without labels?

  6. Time, drift, and monitoring — What changes after a model leaves the notebook?

  7. Interpretability and stakeholder explanation — What explanations are appropriate for different audiences?

  8. Predictive modeling portfolio — What evidence supports a production recommendation?

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

This GitHub Pages site is the learner-facing textbook for AINS6002 Machine Learning and Predictive Modeling. 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.