Continual 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.

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.

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

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

A variant of gradient descent that updates parameters using a randomly selected subset of training data each iteration.

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

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

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

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