Module 8 Instructor Notes

Module 8 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#

  • Notebook state that cannot be reproduced by another practitioner.

  • Silent CPU/GPU tensor mismatch or unintended device transfers.

  • Scaling batch size without checking memory, convergence, or metric comparability.

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.