# Syllabus: AINS6002 Machine Learning & Predictive Modeling

## Catalog Description

Covers supervised, unsupervised, and operational predictive modeling with reproducible 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 | Prediction tasks and data framing | What decision is the model actually supporting? | Lab notebook + assignment brief |
| 2 | Data preparation and feature pipelines | How do preprocessing choices shape model behavior? | Lab notebook + assignment brief |
| 3 | Linear and tree-based baselines | Why do strong baselines matter before complex models? | Lab notebook + assignment brief |
| 4 | Model selection and validation | How do we choose models without overfitting our evidence? | Lab notebook + assignment brief |
| 5 | Unsupervised learning and structure discovery | How can models reveal patterns without labels? | Lab notebook + assignment brief |
| 6 | Time, drift, and monitoring | What changes after a model leaves the notebook? | Lab notebook + assignment brief |
| 7 | Interpretability and stakeholder explanation | What explanations are appropriate for different audiences? | Lab notebook + assignment brief |
| 8 | Predictive modeling portfolio | What evidence supports a production recommendation? | 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.