Pattern Recognition

TL;DR

Trains models to detect and classify recurring structures in datasets so they can make predictions or decisions.
Uses different approaches (e.g., supervised and unsupervised learning) depending on whether labeled examples are available.
Applied across domains such as image and speech recognition, natural language processing, and data mining.

Definition

Pattern recognition is a subfield of machine learning that deals with the identification and classification of patterns in data. It involves training a machine learning model to recognize patterns in data and make predictions or decisions based on those patterns.

Explanation

Pattern recognition encompasses methods that enable models to learn from data and extract regularities that correspond to meaningful categories or behaviors. Approaches vary by the availability of labeled data:

Supervised learning trains models on labeled datasets where the correct output is provided for each example, allowing the model to learn mappings from inputs to known labels.
Unsupervised learning lets models discover structure in unlabeled data, for example by grouping similar examples together.

Both supervised and unsupervised methods can be useful depending on the task: supervised learning is often suited to problems where the desired output is known, while unsupervised learning can be appropriate when the goal is to find unusual or unexpected patterns.

Examples

Supervised learning

A supervised learning model might be trained to recognize handwritten digits by being shown thousands of images of handwritten digits, each labeled with the correct digit. The model learns to recognize patterns in the images that correspond to specific digits, and uses those patterns to make predictions on new, unlabeled images.

Unsupervised learning

An unsupervised learning model can learn to recognize patterns without labeled examples. One example is clustering, where the model groups similar examples together into clusters. For instance, a model might be trained on a dataset of customer data and learn to group customers into different clusters based on their purchasing patterns, without being told which customers belong to which group.

Use cases

Image recognition
Speech recognition
Natural language processing
Data mining

Notes or pitfalls

The choice between supervised and unsupervised learning depends on the specific problem and the availability of labeled data: supervised learning is more appropriate when the desired output is known, while unsupervised learning may be better for discovering unusual or unexpected patterns (e.g., anomaly detection).

Machine learning
Supervised learning
Unsupervised learning
Clustering
Anomaly detection
Image classification
Image recognition
Speech recognition
Natural language processing
Data mining