Session by session
- Session 11 · Ask & understandWeek 6
Features, labels and session-based splitting
Evidence: a question, sketch or new vocabulary
- Session 12 · Build & designWeek 6
Build a small line-sensor dataset
Evidence: a design draft, dataset or build
- Session 13 · ImplementWeek 7
Compare a baseline with a simple classifier
Evidence: a working version, explained once
- Session 14 · Test & improveWeek 7
Evaluate a separate capture session
Evidence: a test log with at least one failure
- Session 15 · Explain & reflectWeek 8
Explain leakage risks
Evidence: an individual explanation
- Local Python/scikit-learn
- recorded sensor CSV
Split by capture session; compare per-class results.
A model, an evaluation study or an AI-checking workflow.
Completion needs the artefact, an honest test log, an individual explanation and no open safety or privacy issue.
Projects in the same block
- RoboticsG11-P01
Line-Sensor Calibration Bench
A calibrated line-sensor array and Arduino input program.
Sessions 01-05All plans - RoboticsG11-P02
Line-Following Control Core
A low-speed line follower with a safe lost-line state.
Sessions 06-10All plans - AIG11-P03
Python Sensor-State Classifier
A reproducible classifier for recorded sensor states.
Sessions 11-15All plans - IntegratedG11-P04
AI Route-State Adviser
A laptop-based AI adviser for a line-following model; physical deployment is supervised.
Sessions 16-20All plans
For school leadersChoose a starting point.
Build from evidence.
Pick the classes and a plan. We map the timetable, kit and safety checks with you, then pilot one class first.
- Prospectus and class-wise plan
- Kit and readiness check
- Pilot one class first
- Evidence at every milestone
Let's plan your pilot.
Share a few details and we will send the right plan for your classes.