Association Rule Learning

Dimensionality Reduction

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

Principal Component Analysis

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

t-SNE

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

UMAP

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

Clustering

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

K-Means Clustering

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

DBSCAN

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

Hierarchical Clustering

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

Collaborative Filtering

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

Content-Based Filtering

A recommendation approach that suggests items similar to those a user has previously liked, based on item attributes.

Matrix Factorisation

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

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

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

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

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

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

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