This chapter introduces Support Vector Machines (SVM), one of the most influential algorithms in supervised machine learning. It begins by presenting the core principles of linear classifiers and the concept of the optimal separating hyperplane, followed by the notions of margin maximization and support vectors. The chapter then discusses soft-margin SVMs and the role of regularization in handling non-separable data. A central focus is given to the kernel trick, which enables the extension of SVMs to nonlinear decision boundaries through kernel functions such as polynomial and radial basis functions. Practical applications in economics and finance are highlighted, showing how SVMs can be used for classification, prediction, and risk assessment tasks.