📗 The quizzes must be completed during the lectures and submitted on TopHat: Link. No Canvas submissions are required. The grades will be updated by the end of the week on Canvas.
📗 Please submit a regrade request if (i) you missed a few questions because you are late or have to leave during the lecture; (ii) you selected obviously incorrect answers by mistake (one or two of these shouldn't affect your grade): Link
The following questions may appear as quiz questions during the lecture. If the questions are not generated correctly, try refresh the page using the button at the top left corner.
📗 [2 points] Move the plus (blue) and minus (red) planes so that they separate the two classes and the margin is maximized.
Plane: 0
Margin: 01 slider
📗 [6 points] A linear SVM (Support Vector Machine) with with weights \(w_{1}, w_{2}, b\) is trained on the following data set: \(x_{1}\) = , \(y_{1}\) = and \(x_{2}\) = , \(y_{2}\) = . The attributes (i.e. features) are two dimensional \(\left(x_{i1}, x_{i2}\right)\) and the label \(y_{i}\) is binary. The classification rule is \(\hat{y}_{i} = 1_{\left\{w_{1} x_{i1} + w_{2} x_{i2} + b \geq 0\right\}}\). Assuming \(b\) = , what is \(\left(w_{1}, w_{2}\right)\) ? The drawing is not graded.
📗 Answer (comma separated vector): .
📗 [2 points] Transform the points (using the kernel) and move the plane such that the plane separates the two classes.
Kernel: 0
Plane: 00 slider
📗 [4 points] Consider a kernel \(K\left(x, y\right)\) = + + , where both \(x\) and \(y\) are positive real numbers. What is the feature vector \(\varphi\left(x\right)\) induced by this kernel evaluated at \(x\) = ?
📗 Answer (comma separated vector): .