Part 2: Draw approximately the output of the SVM algorithm on this dataset.

Polynomial and Higher order Features (3 Points): Let us use polynomial features with an SVM. Consider the dataset shown below. [Hint: The dataset is not separable]. Note that this dataset Figure 1: Two Dimensional Data consists of 2 -dimensional points x=[x1,x2]. Part 1: Write down the SVM loss function (using Hinge Loss) with quadratic features. First write down what will be the features, the dimensionality of the expanded (quadratic) feature set and the loss function. Is the dataset linearly separable with quadratic features