Plotted above is a dataset of one-dimensional samples from two classes. Within that dataset: Sample 1: x=[5],y=1 Sample 2: x=[1],y=+1 Sample 3: x=[4],y=1 Q3 (1pt): Calculate the dot product between all sample pairs: You'd like to be able to separate the samples of the 1 class from the +1 class, but you can see that they aren't linearly separable. You decide to project the data into two dimensions. Updated samples: Sample 1: x=[5,25], y=1 Sample 2: x=[1,1] y=+1 Sample 3: x=[4,16], y=1 Q4 (1pt): Draw where you think the maximal margin boundary should be on the plot Q5 (2pts): Calculate the dot product between all pairs of projected samples, and then re-calculate using the polynomial kernel on the un-projected samples instead