Machine Learning
A family of methods that fit models to data so they generalize to unseen inputs, rather than following hand-written rules.
Definition
Machine learning (ML) builds predictive or generative models by optimizing parameters against data. Instead of encoding rules directly, the engineer chooses a model class, a loss function, and an optimizer, then lets the data determine the parameters.
A useful distinction is between parametric models, which fix the number of parameters in advance, and non-parametric ones, whose complexity grows with the data. The bias a model brings, its inductive bias, is what lets it generalize at all: with no assumptions, no amount of data would justify predicting an unseen input.
The distribution the data is drawn from matters as much as the algorithm. A model that performs well assumes future inputs resemble past ones, an assumption that breaks under distribution shift, when the world changes or the deployment population differs from the training population. Detecting and adapting to such shift, through monitoring and periodic retraining, is a large part of keeping deployed models honest, and it is why a strong test score is a starting point rather than a guarantee.
The three canonical settings are supervised learning (learn a mapping from labeled examples), unsupervised learning (find structure in unlabeled data), and reinforcement learning (learn a policy from reward).
The learning loop
- Split data into training, validation, and test sets.
- Fit parameters on training data by minimizing a loss.
- Tune model choices on validation data.
- Report honest performance on held-out test data.
Why it matters
ML scales to problems where explicit rules are unknown or too numerous, such as image recognition or predicting a plasma diagnostic from sensor traces. Its central hazard is overfitting: memorizing training data instead of learning a generalizable pattern.
Fusion connection
ML regressors map machine settings to predicted performance for Hyperion, letting engineers explore a large parameter space cheaply. Every candidate is still confirmed against first-principles simulation before it informs design.