1. Given are the following 2-dimensional examples from two classes (class I and class II) class I: pl = [1 1], p2 = [-1-1] class II: p3 12 2] Can a perceptron learn to distinguish between them? Yes No 2. (The same question as above but for different examples.) Given are the following 2-dimensional examples from two classes (class I and class II) class I: pl [1 1], p2 class II: p3-[10] [1 -1] Yes No Can a perceptron learn to distinguish between them? 3. If the data is linearly separable, the perceptron will optimize the decision boundary and is guaranteed to find the best possible boundary that separates the examples in a finite number of steps. True False 4. A single perceptron can solve the XOR problem True False 5. The perceptron's learning rule reflects exactly the operation of the human neurons. True False