Module 7 Instructor Notes

Emphasis

This module should feel like applied neural-network engineering, not a survey lecture. Students need to explain why the method fits the data and why the evidence is strong enough for the stated claim.

Common Misconceptions

• Evaluating generated outputs only by subjective preference.

• Using synthetic data without checking privacy leakage or distribution distortion.

• Conflating fluent output with factual or clinically valid output.

When these appear, redirect students to concrete evidence: tensor shapes, loss curves, validation behavior, diagnostic comparisons, or a written assumption log.

Facilitation Plan

• Begin with a five-minute scenario readout and ask students to identify the decision being supported.

• Have students predict expected lab behavior before running code.

• Compare two student modifications and ask which changed a single variable cleanly.

• Require one limitation statement before accepting a performance claim.

Evidence Expectations

A strong submission includes runnable code or clearly reproducible outputs, a comparison point, correct neural-network terminology, and an explicit limitation. A weak submission reports a score or screenshot without connecting it to the design decision.

Extension Options

• Add a baseline that is simpler than the neural model.

• Repeat the run with a different seed and report stability.

• Add a diagnostic plot or tensor-shape table.

• Write a short model-card section for the module artifact.