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[1] [2] [3] In statistics literature, it is sometimes also called optimal experimental design. [4] The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
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 ]
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]
Algorithmic learning theory, from the work of E. Mark Gold; [7] Online machine learning, from the work of Nick Littlestone [citation needed]. While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms.
It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning.
The starting point in learning theory is typically a concept class, a set of possible concepts. Usually a concept is a function on some domain, such as {,}. For example, the concept class could be the set of disjunctive normal form (DNF) formulas on n bits or the set of Boolean circuits of some constant depth. The goal for the learner is to ...