Generalized Additive Models: Applications and Techniques

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Page 3 of 4

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Linear Mixed Model

Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.

Modern Regression: Spring Barley Data

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

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.

Linear Models: Part 1

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Linear Models for Classification: Multi-Class Extensions

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

General Linear Model: Model Selection

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Regression and Classification

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

Modern Regression: Overdispersion and Model Assessment

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

Linear Models: Classification

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