Module 7 · Classical ML
Teach and adapt Module 7
Link the entry page as additional reading/practice. Its reading-site route is /lessons/07-clustering/ relative to the served site base. For a repository link, select a tested commit or release.
Facilitation and access
Use the same outcomes and written evidence for in-person/asynchronous learners. Begin with the foundation bridge, predict-before-run questions, and the shared script/notebook. Discussion has a solo equivalent. Optional depth is skippable, and GenAI is not required.
Interpretation can use named values and exact tables; code/artefacts demonstrate execution or authoring when those are assessed. A screenshot cannot substitute for an explanation, and a script does not prove notebook-interface operation. Verify the learner's actual assistive-technology route and provide private help.
Adaptation decisions
Device clustering would require real permitted telemetry, host/time boundaries, a distance meaningful for those units, and an operational use that does not mistake clusters for threats.
When changing a dataset, define its license/provenance, observation unit, target if any, availability, split, and limits first. Update preprocessing, inference contracts, examples, feedback, and tests together. Preserve the original source and disclose transformations. Do not label a tiny mechanics check as a credible production evaluation.
Outcome/evidence alignment
- M07.1: Distinguish supervised, unsupervised, and reinforcement learning by the information available.
- M07.2: Fit training-only scaling and k-means without quality labels.
- M07.3: Compare predefined cluster counts using a validation geometry criterion.
- M07.4: Describe clusters through counts and original-unit profiles.
- M07.5: Explain why internal scores and observational data do not validate actions or rewards.
Course alignment opportunities: CLO1, CLO3, CLO4, CLO8. Advisory computing-outcome opportunities from the planning register: A1, A2, A3. These are opportunities for evidence, not proof of master's-level attainment or accreditation compliance. The original-module crosswalk remains in the previously prepared planning resources. Confirm adopted program criteria before formal use.
Verification limits
The real-data workflow, public script, and notebook are exercised in Windows/Linux core CI. Exact counts/results are in the verified results table and commit-specific CI. Consult the accessibility evidence log for human interface-review coverage. Public feedback is for self-study; local grading/AI-use rules remain the adopting instructor's responsibility.
Continue the learning route
Use the course review and pilot protocol. Review a changed-case explanation, not only passing visible checks. Preparation and activity times need observation with actual learners.