Active Learning

Machine Learning

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

Supervised Learning

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Unsupervised Learning

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

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Multi-Task Learning

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

Online Learning

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

Batch Learning

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

Ensemble Learning

Combining multiple machine learning models to produce better predictive performance than any single model.

Feature Selection

The process of identifying and selecting the most relevant input variables for a machine learning model.

Epoch

One complete pass through the entire training dataset during the machine learning model training process.

Model Serialisation

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

Model Serving

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

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

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

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

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

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

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

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.

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

Oracle

A service that provides external real-world data to blockchain smart contracts that cannot access off-chain information directly.