Machine learning is a subfield of artificial intelligence in which computer systems improve their performance on a task by learning statistical patterns from data, rather than following rules that are explicitly programmed by hand. It underlies most contemporary Artificial intelligence applications, from spam filters to Large language models.
The term is generally attributed to Arthur Samuel, who used it in 1959 to describe a checkers-playing program that improved through self-play.
Paradigms
Machine learning is commonly divided into three paradigms. In Supervised learning, a model learns from examples paired with correct labels, such as images tagged with object categories. In Unsupervised learning, a model finds structure in unlabeled data, for example by clustering similar items together. In Reinforcement learning, an agent learns by taking actions in an environment and receiving rewards or penalties, a paradigm that produced landmark results such as AlphaGo. A fourth approach, Self-supervised learning, generates its own training signal from unlabeled data, for instance by predicting a hidden word from its context, and is the method behind the pretraining of modern language models.
Core methods
Most machine learning models are trained by defining a Loss function that measures how wrong the model's predictions are, then adjusting the model's parameters to reduce that loss through Gradient descent or a related optimization procedure. Statistical approaches that predate the deep learning era, such as support vector machines developed by Vladimir Vapnik and colleagues, remain in use for smaller or more interpretable problems. Since the 2010s, Neural network models, particularly Deep learning architectures, have become the dominant method for tasks involving images, audio, and text, given sufficient training data and compute.
Practical concerns
A central challenge in machine learning is Overfitting, where a model memorizes patterns specific to its training data rather than learning patterns that generalize to new examples. Practitioners address this through techniques such as regularization, cross-validation, and holding out test data. The quality and scale of training data, along with the choice of model architecture, are typically larger determinants of real-world performance than any single algorithmic trick.
Adoption
Machine learning moved from an academic discipline to a mainstream engineering practice over the 2010s, driven partly by publicly available frameworks, cloud computing, and educators such as Andrew Ng, whose online courses introduced the field to a broad audience. It now underpins search ranking, fraud detection, medical imaging analysis, recommendation systems, and the training of large language models that power modern conversational assistants.
The distinction between machine learning and earlier rule-based software is not always sharp in practice, since many production systems combine learned components with hand-coded business logic, but the defining feature of a machine learning system remains that its behavior is shaped substantially by the data it was trained on rather than by rules a programmer wrote directly.