Maximum Likelihood Estimation (MLE)

Maximum likelihood estimation is one of the most widely used parameter learning methods in statistical modeling. The core idea is to find the parameter values that make the observed data most likely to have occurred. This logic lies at the mathematical foundation of many methods, from logistic regression to Gaussian-based models. MLE is powerful because it is both intuitive and theoretically strong: it systematically searches for the parameters that best explain the data. In machine learning, this logic frequently appears through losses such as negative log-likelihood.