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In the least-squares setting, the optimum parameter is defined as such that minimizes the sum of mean squared loss: < math > \vec{\hat{\beta}} = \underset{\vec{\beta}} \mbox{arg min}\,L(D, \vec{\beta}) = \underset{\vec{\beta}}\mbox{arg min} \sum_{i=1}^{n} (\vec{\beta} \, . \, \vec{x_{i}} - y_i)^2 < /math > Now putting the independent and dependent variables in matrices < math > X < /math > and < math > Y < /math > respectively, the loss function can be rewritten as: < math >...