Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
