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Mathematics

This trunk is a working mathematical toolkit. The goal is not to reproduce a textbook, but to preserve the definitions, intuitions, derivations, and computational patterns that support other branches.

Branches

BranchPrimary connection
CalculusChange, gradients, optimization, and continuous models
Linear AlgebraVectors, transformations, and machine learning
Information Theory & EntropyCompression, uncertainty, and learning objectives
Discrete MathematicsLogic, combinatorics, and computer science
Statistics & ProbabilityInference, uncertainty, and data analysis
Numerical AnalysisReliable computation and approximation
Convex OptimizationStructured optimization problems
Graph TheoryNetworks, algorithms, and relational structures

Suggested Paths

  • For machine learning: Linear Algebra → Statistics & Probability → Calculus → Optimization
  • For algorithms: Discrete Mathematics → Graph Theory → Computer Science
  • For quantitative modeling: Statistics & Probability → Numerical Analysis → Quantitative Finance