Learn ML with Real Code Examples

ML supports functional programming paradigms with first-class functions and immutable data structures.

Its type system automatically infers types, reducing runtime errors.

Widely used in academic research, proof assistants, and language development.

Let-bindings and recursion

Algebraic data types

Polymorphic functions

Pattern matching in function definitions and case analysis

Module system with signatures, structures, and functors

Immutable variables and let-bindings

Recursive function definitions

Pattern matching on data types

Polymorphic types and type inference

Modules and functors for code abstraction

Source file (.ml)

Optional signature files (.mli)

Modules and functor definitions

Test scripts

Documentation and examples

Define algebraic data types

Write functions using recursion and pattern matching

Use REPL to test small expressions

Organize code into modules

Compile and run larger ML programs

Beginner: arithmetic and recursive functions

Intermediate: pattern matching and data structures

Advanced: compiler/interpreter components

Expert: theorem proving and symbolic computation

ML vs Haskell: strict vs lazy evaluation

ML vs Lisp: static vs dynamic typing

ML vs Python: functional vs multi-paradigm scripting

ML vs OCaml: closely related, OCaml adds OO features

ML vs Standard imperative languages: emphasizes immutability and recursion

1973 – ML created as metalanguage for LCF theorem prover

1984 – Standard ML (SML) formalized

1990s – MLton compiler and SML/NJ gained popularity

1996 – OCaml gains traction as ML variant with OO support

2000s–present – ML used in formal methods and compiler research

Type inference: compiler determines types automatically

Pattern matching: dispatch based on data structure forms

Functor: module parameterized by another module

Signature: type specification of a module

Algebraic data type: user-defined composite type