We will add 2 regularization to linear regression. When we have a large number of features compared to instances, regularization can help control overfitting. Ridge regression is linear regression with 2 regularization. The regularization term is sometimes called a penalty term. The objective function for ridge regression is J()=m1i=1m(h(xi)yi)2+T, where is the regularization parameter, which controls the degree of regularization. Note that the bias term (which we included as an extra dimension in ) is being regularized as well as the other parameters. Sometimes it is preferable to treat this term separately. 14. Compute the gradient of J() and write down the expression for updating in the gradient descent algorithm. (Matrix/vector expression, without explicit summation)