AI and machine learning for structural engineers

What this module covers

• Say what machine learning is doing, in terms an engineer can check
• Distinguish supervised, unsupervised and reinforcement learning by what each needs
• Explain overfitting through the training and test error curves
• Show that class imbalance destroys recall while accuracy stays respectable
• Explain why a model gives no signal that it is extrapolating
• State where AI belongs in structural work, and where it does not
• Apply the rule that a deterministic check, where one exists, must have been run

Lessons

1. 18.1

   What it is actually doing

   Fitting a function to samples — and what that implies about every claim made for it.

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2. 18.2

   Overfitting and generalisation

   Why training error falls forever, why test error turns round, and why the gap is invisible from the training data alone.

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3. 18.3

   Bias, and what the model never saw

   Class imbalance, distribution shift, and the two numbers that behave completely differently.

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4. 18.4

   Surrogates, and who is accountable

   A fast approximation to a slow analysis — what it buys, what it costs off the edge of its data, and the rule that governs its use.

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