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Maximum Likelihood Estimation
Machine Learning Mathematical Derivations (6): Logistic Regression and Classification
Complete derivation of logistic regression from sigmoid to softmax, cross-entropy loss, gradient computation, regularization, and multi-class extension with Python verification.
ML Math Derivations (3): Probability Theory and Statistical Inference
Machine learning is uncertainty modeling. This article derives probability spaces, common distributions, MLE, Bayesian estimation, limit theorems and information theory -- the statistical engine behind every ML model.