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\lecture{Machine Learning}{HW10: Ensemble methods, clustering}{CS 689, Spring 2015}

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Hand in via moodle at: \url{https://moodle.umass.edu/course/view.php?id=20836}.
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\bee
\i  For bagging, why is it important to do bootstrap resample (sampling with replacement) rather than
sampling without replacement?

\i Suppose you are training a binary classifier by boosting decision stumps where all features are binary. In other words the weak learners are decision trees of depth one, i.e., consider only one variable. Show the resulting classifier after boosting has the same form as a linear classifier, i.e. $f(\mathbf{x})=\text{sign}({\mathbf{w}^T\mathbf{x}})$ for some weight $\mathbf{w}$.

\i What happens to the objective function for K-means as K increases? Why?

\ene
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