Reinforcement Learning

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.

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.

Epoch

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

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 hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

A form of regression analysis where the relationship between variables is modelled as an nth degree polynomial.

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

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

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

t-Distributed Stochastic Neighbour Embedding — a technique for visualising high-dimensional data in two or three dimensions.

The process of using domain knowledge to create, select, and transform input variables to improve model performance.