Feature Selection

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.

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.

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.

Bootstrap Aggregating — an ensemble method that trains multiple models on random subsets of data and averages their predictions.

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

Combining deep neural networks with reinforcement learning to enable agents to learn complex decision-making from raw sensory input.

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

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

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

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

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