Generate noisy observations (x, t) (training data points), assuming Gaussian noise. Estimate the regression coefficients w by minimizing
(1) the sum-of-squares error.
(2) Re-formulate the problem in a Bayesian approach by introducing a prior distribution p(w|α) over the coefficients and Solve the Bayesian linear regression problem