Logistic regression is a supervised classification model. This article implements binary logistic regression using gradient descent and Newton's method, deriving loss functions, gradients, and Hessian matrices with full Python code.
Multiple linear regression implemented via the normal equation and gradient descent, with matrix calculus derivations and L2 regularization.
Comprehensive course notes on Probability Theory and Mathematical Statistics covering random events, probability distributions, expectation, covariance, law of large numbers, central limit theorem, parameter estimation, and hypothesis testing.