Curriculum
35 topics across 10 tracks (1 planned) — from limits to functional analysis.
Every topic connects forward to formalML topics it enables.
Prerequisite Graph
The full dependency graph — arrows show prerequisites. Filled nodes are published topics.
Limits & Continuity
The rigorous foundation — epsilon-delta definitions, convergence, completeness.
Single-Variable Calculus
Differentiation, integration, and the theorems connecting them.
Multivariable Differential Calculus
Gradients, Jacobians, Hessians — the engine of optimization.
Multivariable Integral Calculus
Multiple integrals, change of variables, and the big theorems of vector calculus.
Sequences, Series & Approximation
Convergence tests, power series, Fourier analysis, and approximation theory.
Ordinary Differential Equations
Existence theorems, linear systems, stability, and numerical methods.
Linear Algebra
Vector spaces, linear maps, matrix algebra, and spectral theory — the algebraic backbone of optimization, statistics, and ML.
Probability Foundations
Kolmogorov axioms, conditional probability, the union bound — a concrete-probability ramp to the measure-theoretic foundations.
Measure & Integration
Sigma-algebras, Lebesgue integral, Lp spaces, and the Radon-Nikodym theorem — the rigorous foundation of probability.
Functional Analysis Essentials
Metric spaces, Banach and Hilbert spaces, calculus of variations.