Bagging

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.

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.

Bandit Algorithm

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

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

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

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.