Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

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Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Maximum Likelihood Estimation: Theory

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Parametric Models

Explores statistical estimation, regression models, and model selection in parametric models.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.