Prerequisites for SGD as a Stochastic Differential Equation

Direct prerequisites (3)

1. Stochastic Differential Equationslayer 3, tier 2
2. Stochastic Gradient Descent Convergencelayer 2, tier 1
3. Fokker–Planck Equationlayer 3, tier 2

Reachable through the chain (24)

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.

• Ito's Lemmalayer 3, tier 2
• Stochastic Calculus for MLlayer 3, tier 3
• Martingale Theorylayer 0B, tier 2
• Measure-Theoretic Probabilitylayer 0B, tier 1
• Gradient Descent Variantslayer 1, tier 1
• Convex Optimization Basicslayer 1, tier 1
• Differentiation in Rnlayer 0A, tier 1
• Sets, Functions, and Relationslayer 0A, tier 1
• Basic Logic and Proof Techniqueslayer 0A, tier 2
• Vectors, Matrices, and Linear Mapslayer 0A, tier 1
• Continuity in Rⁿlayer 0A, tier 1
• Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
• Matrix Operations and Propertieslayer 0A, tier 1
• Concentration Inequalitieslayer 1, tier 1
• Common Probability Distributionslayer 0A, tier 1
• Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
• Random Variableslayer 0A, tier 1
• Kolmogorov Probability Axiomslayer 0A, tier 1
• PDE Fundamentals for Machine Learninglayer 1, tier 2
• Fast Fourier Transformlayer 1, tier 2
• Exponential Function Propertieslayer 0A, tier 1
• Eigenvalues and Eigenvectorslayer 0A, tier 1
• Functional Analysis Corelayer 0B, tier 2
• Inner Product Spaces and Orthogonalitylayer 0A, tier 1