AI / ML & Autonomy
Run a deterministic teaching classifier — rule-based or preset ML — on a telemetry vector; inspect the predicted class, confidence score, and contributing features.
Student can run a classifier on a telemetry example, interpret the predicted class and confidence score, identify the key contributing features, and explain the difference between rule-based and ML-based detection.
Run a deterministic teaching classifier — rule-based or preset ML — on a telemetry vector; inspect the predicted class, confidence score, and contributing features.
Open Anomaly Classifier at `/twin/learn/activities/aiml_normal_vs_abnormal` — interactive scenario picker and dual-classifier comparison (deterministic teaching model; not a production ML system).
For one telemetry vector: write an if-then rule that would classify it correctly; then describe what a pattern-matching model would look for instead.
Teaching-grade software activity slot — not a flight simulator or certified propagator.
Step 1 — Choose a telemetry scenario
| Feature | Value |
|---|---|
| voltage | 7.4 V |
| temperature | 43 °C |
| pointing error | 0.25° |
| backlog | 8 MB |
| packet age | 2 s |
Step 2 — Select classifier type
Classifier output — Rule-Based
Predicted class
Confidence
91% confidence
Top contributing features
Explanation
All threshold rules pass — state classified as nominal.
Classifiers agree ✓
Rule-based: nominal (91%) · Preset ML: nominal (87%)
Self-check · Local only
Local-only. No submission, no grade. Answers revealed here only.
What is the main difference between a rule-based and a preset ML classifier?
What does a 'confidence score' from a classifier mean?
A classifier predicts 'Fault Candidate' for a perfectly normal manoeuvre mode change. What is this called?
Evidence capture · Local only
Local-only. No submission, no backend, no grade. Copy or screenshot to share.
Expected outputs learners should be able to show after the lab (Phase 9 evidence engine preview available).
Choose a telemetry scenario; select rule-based or preset classifier; inspect prediction, confidence, and top features; note where the two classifiers agree or disagree.
Responses are not persisted in this preview unless a specific activity component adds storage later.
Give one example of a telemetry pattern that looks abnormal but is actually expected during a planned mode change — and explain how a classifier might incorrectly flag it.
Use think-aloud protocol: ask students to narrate what each top feature tells the classifier. Stress that confidence ≠ correctness.
Suggested progression from the mission learning path. Links avoid missing activity routes.