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Protocol

Generalization Claims with Explicit Assumptions

Write a short theorem-to-experiment note. Include one valid bound, one empirical plot and one counterexample to an overbroad interpretation.

45–80 active hours60–90 min sessionsAdvanced

← ML stages and entry readiness

Mastery contract

Core protocol · Standard 1.0 · Write a short theorem-to-experiment note. Include one valid bound, one empirical plot and one counterexample to an overbroad interpretation.

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  1. Reasoning — not yet passed

    The proof correctly quantifies over the class, confidence and sampling process, with a clear distinction between realizable and agnostic settings.

  2. Reliable implementation — not yet passed

    Bound calculations include class size and confidence and reproduce a hand-calculated example.

  3. Reproducible experiment — not yet passed

    Report vacuous bounds honestly and demonstrate where the distribution-shift example breaks the assumptions.

  4. Defense and handoff — not yet passed

    Apply the bound to a measured overparameterized-model example, identify each unmet assumption and explain why any vacuous value cannot support a generalization claim.

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 a finite-hypothesis uniform-convergence bound using a concentration inequality and union bound; state every sampling and bounded-loss assumption.

  2. Implement a finite-class learner on a synthetic binary problem and compute the corresponding bound without using test outcomes to pick the bound.

  3. Compare empirical generalization gaps and bounds over 3 sample sizes and 20 seeds; construct a distribution-shift counterexample.

  4. Debug a proof that replaces uniform convergence with pointwise convergence after data-dependent model selection.

  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-4