Math for ML / probability-statistics

Probability and statistics

Random variables, distributions, Bayes, MLE/MAP, confidence intervals, bootstrap, Monte Carlo, and uncertainty.

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Focus
  • likelihood vs probability
  • common distributions
  • hypothesis testing
  • uncertainty estimates
Future feature idea

Distribution, likelihood, confidence interval, bootstrap, and Monte Carlo explorer.

Self-check
  1. What is the core job of "Probability and statistics"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.