Semi-Supervised Learning

Self-Supervised Learning

A learning paradigm where models generate their own supervisory signals from unlabelled data through pretext tasks.

Transfer Learning

A technique where knowledge gained from training on one task is applied to a different but related task.

Meta-Learning

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.

Curriculum Learning

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.

Bagging

Bootstrap Aggregating — an ensemble method that trains multiple models on random subsets of data and averages their predictions.

Bandit Algorithm

An online learning algorithm that balances exploration of new options with exploitation of known good options to maximise reward.

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

A controlled experiment comparing two variants to determine which performs better against a defined metric.

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

A machine learning approach where models discover patterns and structures in data without labelled examples.

A regularised regression technique that adds an L1 penalty, enabling feature selection by driving some coefficients to zero.

t-Distributed Stochastic Neighbour Embedding — a technique for visualising high-dimensional data in two or three dimensions.

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.