Curriculum 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.

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.

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.

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

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

A centralised repository for storing, managing, and serving machine learning features, ensuring consistency between training and inference environments across an organisation.

An ensemble technique that sequentially trains models, each focusing on correcting the errors of previous models.

A machine learning paradigm where models learn from a continuous stream of data, accumulating knowledge over time without forgetting previously learned information.

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

The application of deep neural networks to structured tabular datasets, competing with traditional methods like gradient boosting through specialised architectures and regularisation.

Strategy

A plan of action designed to achieve a long-term or overall aim, involving resource allocation and competitive positioning.