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.