Transfer Learning

Semi-Supervised Learning

A learning approach that combines a small amount of labelled data with a large amount of unlabelled data during training.

Self-Supervised Learning

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

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.

The process of using domain knowledge to create, select, and transform input variables to improve model performance.

A recommendation approach that suggests items similar to those a user has previously liked, based on item attributes.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

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

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

The systematic recording of machine learning experiment parameters, metrics, artifacts, and code versions to enable reproducibility and comparison across training runs.

A method for discovering interesting relationships and patterns between variables in large datasets.