2. Optimization (25 points). Consider a simplified logistic regression problem. Given m training samples (xi,yi),i=1,,m. The data xiR2 (note that we only have one feature for each sample), and yi{0,1}. To fit a logistic regression model for classification, we solve the following optimization problem, where R2 is a parameter we aim to find: max(), where the log-likelhood function ()=i=1m{log(1+exp{Txi})+(yi1)Txi}. 2. (5 points) Write a pseudo-code for performing gradient descent to find the optimizer . This is essentially what the training procedure does. 2. Optimization (25 points). Consider a simplified logistic regression problem. Given m training samples (xi,yi),i=1,,m. The data xiR2 (note that we only have one feature for each sample), and yi{0,1}. To fit a logistic regression model for classification, we solve the following optimization problem, where R2 is a parameter we aim to find: max(), where the log-likelhood function ()=i=1m{log(1+exp{Txi})+(yi1)Txi}. 2. (5 points) Write a pseudo-code for performing gradient descent to find the optimizer . This is essentially what the training procedure does