# 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 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 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?