Module 4: Machine learning as empirical inference

Module 4: Machine learning as empirical inference#

Theme#

Machine learning as empirical inference

Essential Question#

How does learning from data differ from explicit programming?

Module Components#

  • Book prose: conceptual framing, domain scenario, methods, and failure modes

  • Assignment: evidence-backed production of a specific artifact

  • Slides: presentation sequence for seminar or lecture delivery

  • Narration: spoken version of the slide flow

  • Rubric: criteria for evaluating the module artifact

  • Notebook: executable lab aligned with the module theme using synthetic system evidence including task features, model outputs, confidence signals, and review outcomes

Module Artifact#

AI system review package with architecture, evidence, limitations, and deployment recommendation focused on machine learning as empirical inference: Train and compare two baseline models on a small dataset.

Professional Setting#

Students work as if advising an AI review team evaluating a proposed applied AI system before pilot deployment. Their work must be intelligible to technical lead, domain owner, governance reviewer, and end-user representative.

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-4.

  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.