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\title{10-601 Machine Learning: Assignment 2}
% by andrew and william
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\begin{itemize}
\item The assignment is due at 3:00pm (beginning of class) on \textbf{Monday, February 11, 2008}.
\item Since this assignment has an extra problem from last week it will be worth \textbf{125 points}
\item Write your name at the top right-hand corner of each page submitted.
\item Each student must hand in a writeup and their code, where asked. See the course webpage for the code submission and collaboration policies.
\end{itemize}
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\section*{Q1: Maximum likelihood estimators [25 pts]}
\subsection*{(same as Q4 from HW 1)}
\begin{enumerate}
\item Let $X_1, X_2, ..., X_n \backsim Uniform(a,b)$ where $a$ and $b$ are parameters, $a** 0$
\item The examples all lie within some radius $R$ of the origin
\end{enumerate}
Describe an algorithm that will produce a sequence of examples that
force the perceptron algorithm to make a sequence of mistakes of
arbitrary length, $m$, if either of these assumptions (your choice) is
removed (but the data still remain separable).
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