Matrix Factorisation

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.

Association Rule Learning

A method for discovering interesting relationships and patterns between variables in large datasets.

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.

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

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

The tendency of neural networks to completely lose previously learned knowledge when trained on new tasks, a fundamental challenge in continual and multi-task learning.

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

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

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

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

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.