Statistics for Machine Learning — Probability, Distributions, Hypothesis Testing, Bayesian Inference, A/B Testing

Probability, Hypothesis Testing, Bayesian

Author: AI Learning Club Team · Difficulty: Intermediate · Duration: 2 hr 15 min · Modules: 26

Modules

1. Introduction to Probability (5 min)
2. Common Probability Distributions (5 min)
3. Expectation and Variance (5 min)
4. Joint, Marginal, and Conditional Distributions (5 min)
5. Introduction to Hypothesis Testing (5 min)
6. Parametric vs Non-parametric Tests (5 min)
7. P-values and Confidence Intervals (5 min)
8. Multiple Hypothesis Testing (5 min)
9. Introduction to Bayesian Inference (5 min)
10. Bayes' Theorem (5 min)
11. Conjugate Priors (5 min)
12. Markov Chain Monte Carlo Methods (5 min)
13. Introduction to A/B Testing (5 min)
14. Designing A/B Tests (5 min)
15. Analyzing A/B Test Results (5 min)
16. Common Pitfalls in A/B Testing (5 min)
17. Resampling Methods: Bootstrap and Permutation Tests (5 min)
18. Time Series Analysis Basics (5 min)
19. Advanced Topics in Probability (5 min)
20. Copulas and Dependence Structures (5 min)
21. Information Theory Basics (5 min)
22. Entropy and Mutual Information (5 min)
23. Advanced Bayesian Methods (5 min)
24. Hierarchical Models (5 min)
25. Case Studies and Applications (5 min)
26. Resources & References (2 min)

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