Support Vector Machines (SVMs) solve binary classification by finding the maximum-margin separating hyperplane. In the hard margin setting for linearly separable data, the dual formulation reveals that solutions depend only on support vectors, which are training points on the margin. For non-separable data, soft margins introduce slack variables and penalty parameter C, balancing margin width against misclassification tolerance. The kernel trick enables nonlinear decision boundaries through implicit feature mapping. Brief coverage of alternative classifiers includes Naive Bayes, k-Nearest Neighbors, Random Forests, and Neural Networks.

This chapter provides a comprehensive introduction to supervised learning techniques for classification problems. It begins with logistic regression for binary classification, explaining the sigmoid function and gradient ascent optimization. The chapter then covers softmax regression for multi-class problems, followed by k-nearest neighbors (kNN) as an intuitive distance-based classifier.

Decision trees are explored in detail, including entropy, information gain, and the ID3 algorithm, along with derived decision rules and association rules. Random forests are presented as an ensemble method that addresses overfitting by combining multiple decision trees.

The chapter covers Naive Bayes classification based on Bayes’ theorem, despite its "naive" independence assumption. Finally, Support Vector Machines (SVMs) are introduced for both linear and non-linear classification using maximum margin hyperplanes.

Each technique includes hands-on R programming examples with real datasets, practical applications, and exercises to reinforce learning concepts.