Inference: Model Checking

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Generalized Additive Models: Applications and Techniques

Explores Generalized Additive Models, covering basics, smooth functions, penalties, practical examples in R, and linear mixed models.

Generalized Linear Models: Theory and Applications

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

Regression Methods: Model Building and Diagnostics

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Regression and Classification

Explores regression, classification, linear models, decision trees, and random forests in data analysis.

Machine Learning Fundamentals: Regularization and Cross-validation

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Parametric Models

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

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Regression Methods: Spline Smoothing

Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.