Math Solution needed for 1a & b. Please use MATLAB for 1c&d,

1a. The value of a weight vector is given as (w1=3, w2=-2, w0=1) for a linear model with soft threshold (sigmoid) function f(x). Define a decision boundary, where the values of the feature vector x result in f(x)=0.5. Plot the decision boundary in two dimensions.

b. Generating training samples: In two dimensional feature space x: (x1, x2,1), generate 20 random samples, for different values of (x1,x2), that belong to two different classes C1 (1) and C2 (0). The label of each feature vector is assigned so that the samples are linearly separable, i.e., can be separated by a linear model with a soft threshold (sigmoid) function. Plot the samples you generate in a two dimensional plane of (x1,x2).

Hint: You may construct an underlying linear model to cut the plane in two halves. Then generate random samples at either side with proper labels.

c. Construct a quadratic error function using a learn model with a soft threshold (sigmoid) function for augmented feature vectors in n+1 dimensions. Derive a gradient decent algorithm for learning the weights. Write a program using either Matlab or Python to learn the weights using the training samples you generate from Prob. 2. Plot the resulting decision boundary.

d. Consider a linear combination of three radial basis functions. Draw a network structure for the model. Write a (pseudo) algorithm for learning the parameters of the model. (You determine what error function to use, what training samples to use, and write iterative equations for learning the parameters.)