ML (programming language) GudangMovies21 Rebahinxxi LK21

      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. ML provides pattern matching for function arguments, garbage collection, imperative programming, call-by-value and currying. While a general-purpose programming language, ML is used heavily in programming language research and is one of the few languages to be completely specified and verified using formal semantics. Its types and pattern matching make it well-suited and commonly used to operate on other formal languages, such as in compiler writing, automated theorem proving, and formal verification.


      Overview


      Features of ML include a call-by-value evaluation strategy, first-class functions, automatic memory management through garbage collection, parametric polymorphism, static typing, type inference, algebraic data types, pattern matching, and exception handling. ML uses static scoping rules.
      ML can be referred to as an impure functional language, because although it encourages functional programming, it does allow side-effects (like languages such as Lisp, but unlike a purely functional language such as Haskell). Like most programming languages, ML uses eager evaluation, meaning that all subexpressions are always evaluated, though lazy evaluation can be achieved through the use of closures. Thus, infinite streams can be created and used as in Haskell, but their expression is indirect.
      ML's strengths are mostly applied in language design and manipulation (compilers, analyzers, theorem provers), but it is a general-purpose language also used in bioinformatics and financial systems.
      ML was developed by Robin Milner and others in the early 1970s at the University of Edinburgh, and its syntax is inspired by ISWIM. Historically, ML was conceived to develop proof tactics in the LCF theorem prover (whose language, pplambda, a combination of the first-order predicate calculus and the simply-typed polymorphic lambda calculus, had ML as its metalanguage).
      Today there are several languages in the ML family; the three most prominent are Standard ML (SML), OCaml and F#. Ideas from ML have influenced numerous other languages, like Haskell, Cyclone, Nemerle, ATS, and Elm.


      Examples


      The following examples use the syntax of Standard ML. Other ML dialects such as OCaml and F# differ in small ways.


      = Factorial

      =
      The factorial function expressed as pure ML:

      This describes the factorial as a recursive function, with a single terminating base case. It is similar to the descriptions of factorials found in mathematics textbooks. Much of ML code is similar to mathematics in facility and syntax.
      Part of the definition shown is optional, and describes the types of this function. The notation E : t can be read as expression E has type t. For instance, the argument n is assigned type integer (int), and fac (n : int), the result of applying fac to the integer n, also has type integer. The function fac as a whole then has type function from integer to integer (int -> int), that is, fac accepts an integer as an argument and returns an integer result. Thanks to type inference, the type annotations can be omitted and will be derived by the compiler. Rewritten without the type annotations, the example looks like:

      The function also relies on pattern matching, an important part of ML programming. Note that parameters of a function are not necessarily in parentheses but separated by spaces. When the function's argument is 0 (zero) it will return the integer 1 (one). For all other cases the second line is tried. This is the recursion, and executes the function again until the base case is reached.
      This implementation of the factorial function is not guaranteed to terminate, since a negative argument causes an infinite descending chain of recursive calls. A more robust implementation would check for a nonnegative argument before recursing, as follows:

      The problematic case (when n is negative) demonstrates a use of ML's exception system.
      The function can be improved further by writing its inner loop as a tail call, such that the call stack need not grow in proportion to the number of function calls. This is achieved by adding an extra, accumulator, parameter to the inner function. At last, we arrive at


      = List reverse

      =
      The following function reverses the elements in a list. More precisely, it returns a new list whose elements are in reverse order compared to the given list.

      This implementation of reverse, while correct and clear, is inefficient, requiring quadratic time for execution. The function can be rewritten to execute in linear time:

      This function is an example of parametric polymorphism. That is, it can consume lists whose elements have any type, and return lists of the same type.


      = Modules

      =
      Modules are ML's system for structuring large projects and libraries. A module consists of a signature file and one or more structure files. The signature file specifies the API to be implemented (like a C header file, or Java interface file). The structure implements the signature (like a C source file or Java class file). For example, the following define an Arithmetic signature and an implementation of it using Rational numbers:

      These are imported into the interpreter by the 'use' command. Interaction with the implementation is only allowed via the signature functions, for example it is not possible to create a 'Rat' data object directly via this code. The 'structure' block hides all the implementation detail from outside.
      ML's standard libraries are implemented as modules in this way.


      See also


      Standard ML and Standard ML § Implementations
      Dependent ML: a dependently typed extension of ML
      ATS: a further development of dependent ML
      Lazy ML: an experimental lazily evaluated ML dialect from the early 1980s
      PAL (programming language): an educational language related to ML
      OCaml: an ML dialect used to implement Coq and various softwares
      F#: an open-source cross-platform functional-first language for the .NET framework


      References




      Further reading




      External links


      Standard ML of New Jersey, another popular implementation
      F#, an ML implementation using the Microsoft .NET framework Archived 2010-02-18 at the Wayback Machine
      MLton, a whole-program optimizing Standard ML compiler
      CakeML, a read-eval-print loop version of ML with formally verified runtime and translation to assembler

    Kata Kunci Pencarian:

    ml programming languageml programming language examplesml programming language logoml programming language tutorialml programming language compilerml programming language full formml programming language listml programming language meaningml programming language downloadml programming language pdf
    ML (Programming Language) | PDF | Software Development | Mathematical Logic

    ML (Programming Language) | PDF | Software Development | Mathematical Logic

    Ml programming language list - eroamber

    Ml programming language list - eroamber

    Ml programming language list - snothree

    Ml programming language list - snothree

    Ml programming language list - gawerswiss

    Ml programming language list - gawerswiss

    ML Programming – WiseLearnz

    ML Programming – WiseLearnz

    ML Programming – WiseLearnz

    ML Programming – WiseLearnz

    ml-language · GitHub Topics · GitHub

    ml-language · GitHub Topics · GitHub

    Solved Using ML programming language | Chegg.com

    Solved Using ML programming language | Chegg.com

    ML vs Traditional Programming: Which is best for your career?

    ML vs Traditional Programming: Which is best for your career?

    GitHub - PattarapornH/Learn-ML: Leaning ML | From Introduction to ...

    GitHub - PattarapornH/Learn-ML: Leaning ML | From Introduction to ...

    THE BEST ML/AI PROGRAMMING LANGUAGES FOR 2022 - Rent Indian Coders

    THE BEST ML/AI PROGRAMMING LANGUAGES FOR 2022 - Rent Indian Coders

    What’s the Best Programming Language for Machine Learning Applications ...

    What’s the Best Programming Language for Machine Learning Applications ...

    Search Results

    ml programming language

    Daftar Isi

    ML (programming language) - Wikipedia

    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 ...

    10 Best Language for Machine Learning - GeeksforGeeks

    May 22, 2024 · Finding the best programming language for machine learning (ML) is crucial in the ever-changing world of technology and data science. In this article we will look at the Top Programming Languages designed for ML projects, discussing their benefits, available libraries/frameworks, and specific applications.

    7 Top Machine Learning Programming Languages - Codecademy

    Oct 20, 2021 · What are the best programming languages for machine learning? If you’re considering a career in this field, you’re probably wondering which programming language is best for machine learning. While you have many options, here are 7 of the most popular: 1. Python. Python is one of the leading programming languages for its simple syntax and ...

    CSE341 Lecture Notes 2: Introduction to ML - University of …

    ML is the "exemplary" statically typed, strict functional programming language. History of ML Fig. 1 gives an abbreviated family DAG of the ML family, and a few related languages.

    Best language for Machine Learning: Which Should You Learn?

    Oct 6, 2021 · The best language for machine learning depends on the area in which it is going to be applied, the scope of the machine learning project, which programming languages are used in your industry/company, and several other factors.

    Top 6 Programming Languages for Machine Learning in 2025 - Techopedia

    Jan 17, 2025 · The programming language you choose plays a big role in how smoothly and effectively you can build machine learning projects. In this article, we’ll look at the best ML programming languages, what they’re good at, how they’re used, and what makes them popular.

    Standard ML - Loyola Marymount University

    ML was, long ago, a small programming language. Today, ML is the name for a family of languages that include Standard ML (a.k.a SML), Objective CAML (a.k.a OCaml), F#, LazyML, Alice, and Elm. The original ML and its immediate descendants were never really widely used, but they have been enormously influential.

    The Best Languages for Machine Learning Development

    Jan 21, 2025 · The best programming language for machine learning largely depends on your project’s requirements, your team’s expertise, and the specific use case. Choose Python if you need versatility, community support, and rapid prototyping.

    Best Programming Languages for Machine Learning: Top …

    Nov 20, 2024 · Here are the list of Best Programming Languages for Machine Learning such as Python, R, TensorFlow, Keras, PyTorch, Scikit-learn, Julia, C++, Java and MATLAB.

    7 Best Programming Languages for Machine Learning

    Nov 25, 2022 · With hundreds of ML programming languages to choose from, selecting the best option for machine learning projects can be difficult. For someone new to the field, it can also be difficult to determine the most popular language or in-demand programming languages.