Exercise 1 a) Describe the regression task. b) Introduce the linear regression model class under squared loss and derive the optimal solution (in terms of training error, i.e., the least squares algorithm). c) Describe how the approach can be extended in order to avoid the problem of having too large coefficients using regularization