Pattern Matching

Pattern matching is the process of checking a given sequence of data against a set of patterns to determine whether it conforms to expected structures. It appears across multiple domains in computer science, from simple string matching with regular expressions to complex structural matching in functional programming languages like Rust, Haskell, and Scala.

In machine learning and AI, pattern matching takes on a broader meaning. Models learn to recognize patterns in training data - edges and textures in images, syntactic structures in language, or anomalous sequences in time series. Classical approaches like template matching compare input directly against stored templates, while modern deep learning systems learn hierarchical pattern representations automatically through training.

Pattern matching is also fundamental to information retrieval and natural language processing. Search engines match query patterns against document indices, regular expressions extract structured data from unstructured text, and named entity recognition systems match text spans against learned entity patterns. The concept bridges rule-based and learned approaches, making it one of the most broadly applicable ideas in computing.