Unsupervised 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.

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.

The process of adjusting a model's predicted probabilities so they accurately reflect the true likelihood of outcomes, essential for risk-sensitive decision-making.

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

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

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

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

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

A learning approach that combines a small amount of labelled data with a large amount of unlabelled data during training.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.