Method

Method

How this portal is organised, what the levels and stars mean, and the rules its content follows.

Every topic answers the same questions

A formula alone is not understanding. Each topic page is built around eight questions, in this order, so that pages can be compared and nothing essential is skipped:

  1. What is it?
  2. Why does it exist?
  3. What problem does it solve?
  4. How is it interpreted geometrically?
  5. How is it computed?
  6. Where does it show up in computing?
  7. Where does it show up in AI?
  8. What depends on it?

The thesis

  1. Calculus
  2. describes change and accumulation
  3. lets us model systems
  4. lets us optimize
  5. lets us approximate
  6. lets us simulate
  7. and is a core piece of AI, graphics, physics, robotics and scientific computing

Levels

Each topic has one of four levels — how far into a typical university curriculum it sits:

Fundamental

First-year calculus; needed for almost everything else.

Derivative

University

Rest of a standard calculus / analysis degree course.

Gradient

Advanced

Upper-year or graduate material; vector calculus, transforms, modern ML.

Backpropagation

Specialization

Research-level or specialist tools.

Second-order (Hessian-based) optimization

Connections and how strong they are

The map has 640 edges. "Builds on" edges join mathematical topics; "applied in" edges go from mathematics to computing, and each one is rated and explained in a note. No connection is added just to fill the map.

fundamental
The computing topic cannot be stated or computed without it.
frequent
Used routinely in practice.
advanced
Appears in deeper treatments, proofs or state-of-the-art methods.
indirect
Real but tangential; worth knowing, not essential.
historical
Mattered for how the field developed, less for how it works today.

Stars (1–5) say how much the computing topic really depends on the mathematical one. Example:

Strongest connection between each area and each domain

Computed from the data: each cell is the strongest single "applied in" edge from a topic of the row to a topic of the column.

λ = Scientific computing and algorithmsℒ = AI and machine learning3D = Computer graphics⚙ = Robotics and control⚛ = Physics and simulation∿ = Signals, media and vision⇄ = Optimization and systems⊕ = Cryptography and securityψ = Quantum computing and physics

How it is built

  • All content lives in data files (one YAML file per area), not in components; every user-facing text exists in English and Spanish, and the build fails if a translation, a reference or a formula is broken.
  • Formulas are written in TeX and rendered to MathML at build time. There is no backend, no tracking and a strict Content Security Policy.
  • The prerequisite graph is checked to be acyclic, which is what lets every topic page compute "the full path to this topic" and "where is it used" automatically.
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