Prerequisite chain
Prerequisites for PDE Fundamentals for Machine Learning
Topics you need before working through PDE Fundamentals for Machine Learning. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (5)
- Fast Fourier Transformlayer 1, tier 2
- Eigenvalues and Eigenvectorslayer 0A, tier 1
- Stochastic Differential Equationslayer 3, tier 2
- Measure-Theoretic Probabilitylayer 0B, tier 1
- Functional Analysis Corelayer 0B, tier 2
Reachable through the chain (10)
These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.
- Exponential Function Propertieslayer 0A, tier 1
- Matrix Operations and Propertieslayer 0A, tier 1
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Ito's Lemmalayer 3, tier 2
- Stochastic Calculus for MLlayer 3, tier 3
- Martingale Theorylayer 0B, tier 2
- Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
- Inner Product Spaces and Orthogonalitylayer 0A, tier 1
- Vectors, Matrices, and Linear Mapslayer 0A, tier 1