we showed that the Logistic Regression for binary classification boils down to solving the following optimization problem (training error) over n training samples:

n f(w) = Clog (1 +e yiwx;) i=1 a) Compute the gradient of f (w). b) Please write the pseudocode for using GD to optimize the f(w). c) Argue that if data is linearly separable (i.e., can be perfectly classified by a linear model) in the solution obtained by GD, some of the coefficients converge to infinity