Supervised Learning
Training ML models on labelled input-output pairs to learn a function mapping inputs to target outputs.
Definition
Supervised learning is the most common ML paradigm: given a labelled training set {(x₁, y₁), ..., (xₙ, yₙ)}, learn a function f(x) → y that generalises to unseen inputs. Labels can be continuous (regression: predict house price) or categorical (classification: spam/not spam, image category).
The learned function is evaluated on a held-out test set to estimate generalisation performance. Overfitting (performing well on training but poorly on test) is controlled via regularisation (L1/L2 penalties, dropout), data augmentation, early stopping, and cross-validation.
Classic supervised algorithms include linear/logistic regression, decision trees, random forests, SVMs, and gradient boosted trees (XGBoost, LightGBM) — still widely used for tabular data. Deep learning is now dominant for perception tasks (images, text, audio) with enough data.
Examples
- Email spam classifier
- Image classification
- Loan default prediction
- Medical diagnosis from images
Related Terms
Machine Learning (ML)
A subset of AI where systems learn patterns from data rather than following explicitly programmed rules.
Deep Learning
A subset of machine learning using neural networks with many layers to learn complex hierarchical representations.
Data Labeling / Annotation
The process of tagging raw data with labels, bounding boxes, or other metadata to create supervised learning training datasets.
Unsupervised Learning
Finding patterns, structure, or representations in data without labelled examples.