Motivate the Support Vector Machine (SVM) method from a theoretical point of view
Exercise:
Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for both regression and classification tasks. From a theoretical point of view, SVMs are motivated by the desire to find the best separating hyperplane between two classes of data points. The objective is to optimize a cost function, which captures the margin of separation between the two classes. Once a hyperplane is found, it can be used to classify future data points by determining which side of the hyperplane they fall on. SVMs also have the ability to handle non-linear data by using the Kernel Trick, which allows the data to be transformed into a higher dimensional space where the data can be linearly separated. This makes SVMs a powerful tool for dealing with complex datasets.