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Protocol

Clustering and Latent Structure Experiments

Analyze an unlabeled dataset and propose a useful grouping or anomaly workflow. Make the ambiguity explicit and compare at least two defensible interpretations.

35–65 active hours60–90 min sessionsIntermediate

← ML stages and entry readiness

Mastery contract

Core protocol · Standard 1.0 · Analyze an unlabeled dataset and propose a useful grouping or anomaly workflow. Make the ambiguity explicit and compare at least two defensible interpretations.

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  1. Reasoning — not yet passed

    Derive the objectives and distinguish a low-dimensional projection from evidence of a true generative factor.

  2. Reliable implementation — not yet passed

    PCA reconstruction matches a reference and k-means objective is nonincreasing after each complete assignment/update iteration.

  3. Reproducible experiment — not yet passed

    Compare declared clustering/reconstruction baselines across recorded seeds and scaling choices; retain objective, stability and failure measurements, separating any label-based external evaluation.

  4. Defense and handoff — not yet passed

    Defend a clustering or anomaly decision while stating what cannot be inferred from an unlabeled dataset.

All four criteria must pass. Activity completion does not satisfy them.

An independent reviewer reruns your work and varies something you did not rehearse: a fresh input, a different working directory, or a declared edge case.

Pilot decisions are provisional, not external credentials. For an appeal or sensitive artifact, contact your designated pilot operator with the submission ID. Export your assessment history.

What you will do

6 total

  1. Derive the PCA reconstruction objective and k-means alternating minimization; state which transformations change distances.

  2. Implement PCA with SVD and k-means without clustering libraries; handle empty clusters and compare with trusted implementations.

  3. Compare 3 cluster counts over 10 initializations and 2 feature scalings; measure reconstruction, cluster stability and sensitivity of anomaly rankings.

  4. Debug PCA without centering and an anomaly threshold selected after reading test labels.

  5. Write a 500–900 word technical report linking the reasoning, code, measurements, and limitations; include the project decision below.

  6. Assess all 4 mastery criteria against saved artifacts, request an independent review, and repeat each failed criterion on a fresh example.

Essentials

01

A reading session alone never satisfies a mastery criterion.

02

Store source references, assumptions, and artifact paths beside every result.

03

Keep an untouched check case that differs from the worked example.

04

Record all attempts, including failures and results that contradict your prediction.

05

Use the stated comparison conditions; document every deviation before drawing a conclusion.

Protocol guardrails

Do

  • +State the expected result before running the comparison.
  • +Keep one minimal reproducible failing case when debugging.
  • +Record environment versions and the exact command used.
  • +Compare explanations with saved intermediate values.
  • +Ask a reviewer to challenge the weakest assumption.

Don't

  • ×Do not copy a worked solution and present it as an independent implementation.
  • ×Do not tune against held-out evaluation outcomes.
  • ×Do not report only the best seed or discard inconvenient runs.
  • ×Do not equate elapsed hours or a completed run with a passed assessment.
  • ×Do not conceal reduced-scale experiments behind claims about the original full-scale result.

Protocol authorship

Written by NuthinButta as instructional design. The teaching sources are the official references linked in each milestone; the exercises, workload and pass thresholds are ours, not their authors'.

machine-learningml-coreml-stage-3