Self-Supervised Learning

Semi-Supervised Learning

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

Transfer Learning

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

Meta-Learning

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.

Curriculum Learning

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

Bagging

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

Bandit Algorithm

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

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

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

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

A classification algorithm that models the probability of a binary outcome using a logistic function.

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

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

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.