Multi-Task 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.

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.

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

A supervised learning algorithm that finds the optimal hyperplane to separate different classes in high-dimensional space.

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

t-Distributed Stochastic Neighbour Embedding — a technique for visualising high-dimensional data in two or three dimensions.

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

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

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

The process of using domain knowledge to create, select, and transform input variables to improve model performance.