Machine Learning Basics
Machine learning fits models to data so they can make predictions on inputs they have not seen, guided by measured error.
Learning from examples
Instead of being programmed with explicit rules, a machine-learning model is fit to examples. It adjusts internal parameters to reduce the difference between its predictions and the known answers in a training set, then is used to predict on new inputs.
Three broad settings
- Supervised learning: learn from labeled input–output pairs.
- Unsupervised learning: find structure in unlabeled data.
- Reinforcement learning: learn a policy by trial, reward, and feedback.
The central danger: overfitting
A model with enough flexibility can memorize its training data, achieving low training error while performing poorly on new data. This is overfitting. Guarding against it — with held-out validation data, regularization, and simpler models — is the core skill of practical machine learning.
Generalization and its limits
The value of a model is how well it generalizes to data drawn from the same distribution as its training set. When the real world drifts away from that distribution, performance degrades, often silently. Monitoring for this drift is part of deploying any learned system.
Its role, kept honest
In a fusion plant, machine learning is well suited to estimation, anomaly detection, and accelerating simulation as a surrogate. It is kept in advisory and offline roles, with deterministic logic guarding anything that acts on hardware, and with every trained model versioned and reproducible like other engineering software.