Dimensionality Reduction

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.

Matrix Factorisation

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

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

Combining deep neural networks with reinforcement learning to enable agents to learn complex decision-making from raw sensory input.

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

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

A machine learning approach where models discover patterns and structures in data without labelled examples.

The process of adjusting a model's predicted probabilities so they accurately reflect the true likelihood of outcomes, essential for risk-sensitive decision-making.

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

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