Learn ML with Real Code Examples

Pure functional core with immutable data

Type inference engine in compiler

Pattern matching and algebraic data type evaluation

Module and functor system for code organization

Optional imperative features (refs, arrays) in some ML variants

ML programs compiled or interpreted

Type system ensures compile-time correctness

Pattern matching evaluated at runtime

Modules encapsulate state and logic

Recursion and functional constructs drive computation

Pure functional core

Module-based abstraction

Functor-driven parameterization

Algebraic data type-driven control flow

REPL-driven iterative development

Theorem provers (LCF, HOL, Isabelle)

Symbolic computation systems

Compiler construction for new languages

Research prototypes in functional programming

Algorithmic problem-solving frameworks

Functional programming with first-class functions

Strong static typing with type inference

Pattern matching for safe control flow

Immutability as default

Module and functor system for abstraction

Modularize large programs with functors

Optimize recursive functions with tail calls

Use arrays for heavy numeric computation

Profile programs for performance bottlenecks

Leverage multicore support in OCaml variants

Port legacy ML code to OCaml or F# for modern support

Refactor REPL scripts into modular projects

Maintain signature (.mli) files for interface clarity

Use modern build tools (dune for OCaml)

Document original logic for reproducibility