Machine learning (ML) is the subfield of artificial intelligence concerned with algorithms that learn patterns from data and improve at a task with experience, rather than being explicitly programmed with rules. Instead of an engineer writing the logic, the engineer specifies a model and an objective, and the model adjusts its internal parameters to fit examples. The central scientific question is not whether a model fits the data it has seen, but whether it generalises to data it has not.
Features, labels and the learning objective
A machine-learning problem is usually framed in terms of features (the input variables describing each example) and, for supervised tasks, labels (the target output to be predicted). For a model predicting house prices, features might include floor area and location, and the label is the sale price. Learning means searching for model parameters that minimise a loss function measuring the gap between predictions and the truth.
Machine learning is one paradigm within the broader discipline described in our explainer on artificial intelligence definition and history. Where symbolic AI encodes knowledge by hand, ML infers it statistically from examples.
The three main paradigms
Machine learning is conventionally divided into three families, distinguished by what kind of feedback the algorithm receives.
| Type | Data used | Goal | Typical examples |
|---|---|---|---|
| Supervised learning | Labelled examples (features + targets) | Predict a label for new inputs | Classification, regression |
| Unsupervised learning | Unlabelled data | Discover structure | Clustering, dimensionality reduction |
| Reinforcement learning | Rewards from an environment | Learn a policy that maximises long-term reward | Control, game playing, sequential decisions |
Supervised learning trains on examples paired with correct answers and learns to predict those answers for unseen inputs; classification predicts categories and regression predicts continuous values. Unsupervised learning works with unlabelled data and seeks hidden structure, for instance grouping similar items (clustering) or compressing many variables into a few (dimensionality reduction). Reinforcement learning learns by trial and error: an agent takes actions, receives rewards or penalties, and gradually improves a policy that maximises cumulative reward.
The train, validation and test split
To estimate how well a model will generalise, data is partitioned into three disjoint sets. The training set is used to fit the model’s parameters. The validation set is used to tune choices the algorithm does not learn directly, such as model size or learning rate (the hyperparameters), and to compare candidate models. The test set is held back and used only once, at the end, to give an unbiased estimate of performance on unseen data.
The cardinal rule is that the test set must not influence training or model selection. Repeatedly peeking at the test set leaks information and inflates reported performance, a subtle but common source of irreproducible results. We discuss safeguards at length in our guide to reproducibility of machine learning research.
Overfitting and generalisation
Overfitting occurs when a model learns the noise and idiosyncrasies of its training data rather than the underlying pattern, performing well on training examples but poorly on new ones. The opposite failure, underfitting, occurs when a model is too simple to capture the real structure. The art of machine learning lies in finding the balance, the so-called bias-variance trade-off, that yields the best generalisation to unseen data. Techniques such as regularisation, early stopping and cross-validation all serve this goal.
Why method reporting matters
Because performance depends so heavily on the data split, the loss function and the hyperparameters, a machine-learning result is only as credible as its reporting. Standardised vocabulary, captured in the casrai.org research dictionary, helps authors describe their methods consistently, and contribution frameworks such as CRediT help assign credit for the data, software and analysis work involved. Coverage of these issues continues in our AI and ML research outputs category.
Frequently asked questions
What is the difference between supervised and unsupervised learning?
Supervised learning trains on data with known correct answers (labels) and predicts those answers for new inputs. Unsupervised learning works with unlabelled data and instead discovers structure, such as clusters or compressed representations, without a target to predict.
Why split data into training, validation and test sets?
The training set fits the model, the validation set tunes hyperparameters and compares models, and the held-out test set gives an unbiased estimate of real-world performance. Mixing these roles inflates results and undermines reproducibility.
What is overfitting?
Overfitting is when a model memorises the noise in its training data and therefore performs well on that data but poorly on new examples. The goal of machine learning is generalisation, not memorisation.
Is machine learning the same as artificial intelligence?
No. Machine learning is a subfield of artificial intelligence focused on learning from data. AI also includes symbolic reasoning, search and planning that do not learn from examples.
