This document provides an overview of support vector machines (SVMs), including: - SVMs find the maximum-margin hyperplane between two classes during training to create classifiers with good generalization. - The kernel trick allows transforming data into a higher-dimensional space to find separable hyperplanes when data is not linearly separable in its original space. - Soft-margin SVMs allow some misclassified examples to improve accuracy when data is not perfectly separable. - Tutorial tasks demonstrate using LIBSVM to classify toy datasets and evaluate SVMs by varying parameters like kernel type, cost, and gamma values.