Generalized Linear Models II: GLM Extensions

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores marginal models in modern regression, emphasizing interpretation and application in statistical analysis.

Explores Poisson GLM for analyzing count data in categorical analysis, focusing on contingency tables and Bayesian inference.

Explores overdispersion, model assessment, and regression techniques for count data.

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

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

Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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