Meta-Learning

Semi-Supervised Learning

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

Self-Supervised Learning

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

Transfer Learning

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

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

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

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

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

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

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