Machine Learning

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.

Active Learning

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

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.

Synthetic Minority Over-sampling Technique — a method for addressing class imbalance by generating synthetic examples of the minority class.

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

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

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

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

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

A versioned catalogue of trained machine learning models with metadata, lineage, and approval workflows, enabling reproducible deployment and governance at enterprise scale.

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