Derive a high probability bound on |ˆθ −θ_|. 

1. Regularization for Maximum Likelihood: Consider the following regularized loss minimization: 1 m _m i=1 log(1/ θ [xi ])+ 1 _ log(1)+log(1/(1−θ)) _

. _ Show that the preceding objective is equivalent to the usual empirical error had we added two pseudoexamples to the training set. Conclude that the regularized maximum likelihood estimator would be _ Derive a high probability bound on |ˆθ θ_|. Hint: Rewrite this as |ˆθ −E[ˆθ ]+ E[ˆθ ]−θ_| and then use the triangle inequality and Hoeffding inequality. _ Use this to bound the true risk. Hint: Use the fact that now ˆθ ≥ 1 m+2 to relate |ˆθ θ_| to the relative entropy.

find the cost of your paper

Suggest a modification of the binary search algorithm that emulates this strategy for a list of names.

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Explain why insertion sort works well on partially sorted lists.

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Draw a class diagram that shows the relationships among the classes in this new version of the system

Jack decides to rework the banking system, which already includes the classes BankView, Bank, SavingsAccount, and RestrictedSavingsAccount. He wants to add another class for checking accounts. He sees that savings….