(c) Let the dataset D be yi 3 (0.5, 0.5) 1 5(0.5,1) 1 Run, by hand, the perceptron algorithm on these five data points. Record your computations for each time step (data point examined, activation score for that data point and current weight vector, the predicted value, and the new weights) in the following table t weights w(t) Data point 1%N) Activation score Predicted value y New weights w(t+ 1 4 6 For t 0, please fix w(t) to have the correct dimensionality. The strange indexing x(1%NH simply says to iterate through the data in the order provided above. Note the second column of each row should equal the last column of the previous row. (c) Let the dataset D be yi 3 (0.5, 0.5) 1 5(0.5,1) 1 Run, by hand, the perceptron algorithm on these five data points. Record your computations for each time step (data point examined, activation score for that data point and current weight vector, the predicted value, and the new weights) in the following table t weights w(t) Data point 1%N) Activation score Predicted value y New weights w(t+ 1 4 6 For t 0, please fix w(t) to have the correct dimensionality. The strange indexing x(1%NH simply says to iterate through the data in the order provided above. Note the second column of each row should equal the last column of the previous row