UMAP

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.

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.

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

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

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

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

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

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

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

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