Markov Decision Process

Deep Reinforcement Learning

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

A game-theoretic approach to explaining individual model predictions by computing each feature's marginal contribution, based on Shapley values from cooperative game theory.

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

The systematic recording of machine learning experiment parameters, metrics, artifacts, and code versions to enable reproducibility and comparison across training runs.

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

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

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

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.