Back to Article
Week 14 Summary: Course Recap — From Materials Data to Trustworthy Models
Download Notebook

Week 14 Summary: Course Recap — From Materials Data to Trustworthy Models

Cross-Book Summary

1. The Course in One Map

  • One Arc: raw materials data → representations → models → uncertainty and trust.
  • Units 1–3 (Data foundation): small data, measurement chains, leakage-safe validation.
  • Units 4–5 (Representation): stereology to learned embeddings; unsupervised clustering.
  • Units 6–9 (Learning under constraints): transfer learning, time series, inverse problems, spectral signals.
  • Units 10–12 (Modern architectures & trust): transformers, physics-informed constraints, uncertainty quantification.

2. The Four-Question Framework

Every unit is recapped through four checkpoints: 1. What question did it solve? 2. What method & central equation answers it? 3. When do you reach for it — and where does it break? 4. What must you be able to do in the exam?

3. The Decision Guide

  • Twelve real lab scenarios mapped to method and unit, e.g.:
    • Small tabular data with error bars → Gaussian Process (U12).
    • Streaming sensor data, decisions on forecasts → probabilistic LSTM (U7).
    • Long-range image correlations, pretraining available → ViT (U10).
    • Known PDE, sparse sensors → PINN (U11).
    • No labels at all → clustering / autoencoders (U5).
  • Two precursor questions: “Where did this data come from?” (U1–3) and “How wrong can I afford to be?” (U12).

90-Minute Lecture Strategy

Part 1: The Map

  • The 14-week / 12-unit arc as one data-to-trust pipeline.

Part 2: Unit-by-Unit Recap

  • Each unit’s question, method, equation, and failure modes.

Part 3: Synthesis

  • The decision-guide table: task → tool → unit.
  • Exam scope and mini-project rubric.

Quarto Website Update (Summary)

Summary for ML-PC Week 14: - Recaps Units 1–12 with a four-question framework (question, method & equation, when to use, exam competence). - Provides a decision guide mapping twelve lab scenarios to the right method and unit. - Anchors deployment on two questions: data provenance and acceptable error budget. - Closes with exam scope and the mini-project rubric: ML amplified the materials scientist, it did not replace them.