Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Related lectures (31)

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Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.