I would like to get this question answered from my Machine Learning subject.

Consider the following training data: x = (-1,1) Y2 = 1 xy=(1,-1) M = 1 X=(-1,-1) 83 x2 = Run the manually the perception algorithm usuing initial parameters a (0) = 0 a (0) = (1, o) & a fixed length of step l=1. Plot the designed perceptron classifier. (8) same initial By assuming the parameters, find the ou allows the Condition the fixed step length I that perceptron algorithm to find solution after a iteration Single (5) Q200) For the same initial parameters what happens fixed with the show perception ave nistead, Step length bat, algorithm if the training data X, - (-1,-1) anna K = (1,1) 732 20 X = (1,1) Ya = 1 Xy = (1,-1) My = 0 E can you fix this by changing e on the initial parameters? Consider the following training data: x = (-1,1) Y2 = 1 xy=(1,-1) M = 1 X=(-1,-1) 83 x2 = Run the manually the perception algorithm usuing initial parameters a (0) = 0 a (0) = (1, o) & a fixed length of step l=1. Plot the designed perceptron classifier. (8) same initial By assuming the parameters, find the ou allows the Condition the fixed step length I that perceptron algorithm to find solution after a iteration Single (5) Q200) For the same initial parameters what happens fixed with the show perception ave nistead, Step length bat, algorithm if the training data X, - (-1,-1) anna K = (1,1) 732 20 X = (1,1) Ya = 1 Xy = (1,-1) My = 0 E can you fix this by changing e on the initial parameters