Collaborative Filtering

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.

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 situation where the distribution of classes in a dataset is significantly skewed, with some classes vastly outnumbering others.

Techniques that artificially increase the size and diversity of training data through transformations like rotation, flipping, and cropping.

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

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

An online learning algorithm that balances exploration of new options with exploitation of known good options to maximise reward.

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

One complete pass through the entire training dataset during the machine learning model training process.

Continuous observation of deployed machine learning models to detect performance degradation, data drift, anomalous predictions, and infrastructure issues in production.