Epoch

Machine Learning

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

Supervised Learning

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

Unsupervised Learning

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

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Multi-Task Learning

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

Online Learning

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

Batch Learning

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

Active Learning

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

Ensemble Learning

Combining multiple machine learning models to produce better predictive performance than any single model.

Feature Selection

The process of identifying and selecting the most relevant input variables for a machine learning model.

Model Serialisation

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

Model Serving

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

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

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

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

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

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.