Question: derive the regression coefficient parameter, hat { beta } by minimzing the sum of squared errors with L 2 regularization of the regression

derive the regression coefficient parameter, \hat{\beta} by minimzing the sum of squared errors with L2 regularization of the regression coeffiecients (under a scalar regularization parameter \lambda). This is known as ridge regression. First, write the objective function you are optimizing. Then solve for the optimal solution (this is available in closed form)

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