You have labeled data (xi, yi), i = 1, . the form, ,...,n and you want...

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You have labeled data (xi, yi), i = 1, . the form, ,...,n and you want to fit an linear basis function regression model of ji = Σω;e j=0 where x, and y, are scalars. To do this, you will form a matrix (x) = [po(x),..., a(x)] and then solve j = Φw for the weights that minimize the mean squared error. Part 1: Entries of the design matrix Given training data -jx; ((x1, y₁), (x2, y2), (3, 3)) write out the entries of the design matrix you would use to fit the model above, for d = 2. You have labeled data (xi, yi), i = 1, . the form, ,...,n and you want to fit an linear basis function regression model of ji = Σω;e j=0 where x, and y, are scalars. To do this, you will form a matrix (x) = [po(x),..., a(x)] and then solve j = Φw for the weights that minimize the mean squared error. Part 1: Entries of the design matrix Given training data -jx; ((x1, y₁), (x2, y2), (3, 3)) write out the entries of the design matrix you would use to fit the model above, for d = 2.

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