01Statistics for ML - Module OverviewHow statistical theory powers ML model evaluation, A/B testing, and production AI systems. Module map, prerequisites, and learning objectives.02Estimation Theory - MLE, MAP, and the Foundations of ML TrainingMaster Maximum Likelihood Estimation and Maximum A Posteriori estimation. Understand why cross-entropy loss IS negative log-likelihood, and how bias-variance tradeoff applies to estimators.03Hypothesis Testing - p-values, t-tests, and Model ComparisonMaster hypothesis testing for ML engineering - correct interpretation of p-values, Type I/II errors, t-tests, chi-squared tests, and multiple testing corrections for model comparison.04Confidence Intervals - Quantifying Uncertainty in Model MetricsMaster confidence intervals for ML engineering - correct interpretation of CIs, construction for means and proportions, bootstrap CIs, and uncertainty quantification for model evaluation metrics.05Bootstrap and Resampling - Robust Uncertainty Estimation for MLMaster bootstrap resampling, permutation tests, jackknife, and cross-validation as statistical tools for ML model evaluation. Build everything from scratch in NumPy.06Regression Analysis - OLS, Logistic Regression, and Regularised ModelsDeep dive into linear regression OLS derivation, multiple regression, R-squared, logistic regression as a GLM, and Ridge/Lasso from a statistical perspective.07ANOVA and Experimental Design - Comparing Multiple Models and A/B TestsMaster Analysis of Variance, F-statistics, one-way and two-way ANOVA, and rigorous A/B test design for ML model comparison and hyperparameter ablations.08Causal Inference Basics - Why Correlation Misleads and Online A/B Tests WinUnderstand the potential outcomes framework, confounders, average treatment effect, difference-in-differences, and why offline evaluation of recommendation systems fails.09Statistical Power and Sample Size - How Many Samples Do You Actually Need?Master statistical power, effect size, sample size calculation, and power analysis for ML experiments and A/B tests. Know exactly when to stop an experiment and how many examples you need to detect model improvements.
