Vectors are commonly used in machine learning as they lend a convenient way to organize data. Often one of the very first steps in making a machine learning model is vectorizing the data.
They are also relied upon heavily to make up the basis for some machine learning techniques as well.
Vector Definition | DeepAI
In machine learning , support vector machines ( SVMs , also support vector networks ) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis
Support vector machine - Wikipedia
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Feature (machine learning) - Wikipedia
Machine learning is a field of inquiry devoted to understanding and building methods that "learn" – that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine learning - Wikipedia