Module 7: Generative models and applications

Theme

Generative models and applications

Essential Question

What distinguishes discriminative training from generative modeling?

Professional Scenario

A team is considering generated text, images, or synthetic records and needs to distinguish useful generation from unsupported claims.

Learning Outcomes

By the end of this module, students will be able to:

• explain the core technical idea in precise neural-network vocabulary

• connect architecture and training choices to the shape of the data and task

• run or interpret the module lab as reproducible evidence

• identify limitations, failure modes, and next steps for a defensible experiment

Module Components

• Book prose for conceptual framing and practitioner patterns.

• Assignment notebook for the graded artifact: discriminative versus generative comparison.

• Slides and narration for structured teaching.

• Lab notebook for hands-on experimentation.

• Rubric for grading technical correctness, evidence, tradeoffs, risk analysis, and communication.

Use This Module in Order

1. Read the learning chapter.

2. Review the slide deck with the matching narration.

3. In Populi, open the private student-repository link for this course and enter modules/module-7.

4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.