Machine Learning

machine-learning

Definition

Machine Learning

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without explicit instructions, relying instead on patterns and inference. Formally, a computer program is said to learn from experience $E$ with respect to some class of tasks $T$ and performance measure $P$, if its performance at tasks in $T$, as measured by $P$, improves with experience $E$.

Taxonomy

Learning Paradigms (Categorisation by Feedback): Machine learning is typically categorised into three primary paradigms: supervised learning, where the model learns from labelled data; unsupervised learning, where the model identifies latent structures in unlabelled data; and reinforcement learning, where an agent learns to make decisions by interacting with an environment to maximise a cumulative reward.

Model Parameterisation (Structural Representation): Models are often represented by a parameter vector $\theta \in \Theta$, where $\Theta$ is the parameter space. The learning process involves searching for an optimal ${\theta }^{\ast }$ that minimises an empirical risk function $\hat{R}(\theta )$ based on the provided experience.