Prerequisite chain
Prerequisites for Stochastic Processes for ML
Topics you need before working through Stochastic Processes for ML. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (3)
- Measure-Theoretic Probabilitylayer 0B, tier 1
- Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
- Concentration Inequalitieslayer 1, tier 1
Reachable through the chain (5)
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.
- Random Variableslayer 0A, tier 1
- Kolmogorov Probability Axiomslayer 0A, tier 1
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Common Probability Distributionslayer 0A, tier 1