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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Megalitre or megaliter (ML, Ml, or Mℓ), a unit of volume; Millilitre or milliliter (mL, ml, or mℓ), a unit of volume; Millilambert (mL), a non-SI unit of luminance; Richter magnitude scale (M L), used to measure earthquakes; Megalangmuir (ML), a unit of exposure of a surface to a given chemical species (convention is 1 ML=monolayer=1 Langmuir)
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
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It follows, therefore, that 1000th of a litre, known as one millilitre (1 mL), of water has a mass of about 1 g; 1000 litres of water has a mass of about 1000 kg (1 tonne or megagram). This relationship holds because the gram was originally defined as the mass of 1 mL of water; however, this definition was abandoned in 1799 because the density ...
ML (Meta Language) is a general-purpose, high-level, functional programming language.It is known for its use of the polymorphic Hindley–Milner type system, which automatically assigns the data types of most expressions without requiring explicit type annotations (type inference), and ensures type safety; there is a formal proof that a well-typed ML program does not cause runtime type errors. [1]
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). [1] It can also improve the stability and accuracy of ML classification and regression algorithms. Hence, it is prevalent in supervised learning for converting weak learners to strong learners. [2]