\documentclass[a4paper]{article}

\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{float}
\usepackage{geometry}

\title{Exercise: Machine Learning}
\author{Mart Lubbers}
\date{\today}

\begin{document}
\maketitle
\subsection*{Chapter 5: Machine Learning}
\begin{table}[H]
	\centering
	\begin{tabular}{lll}
		\toprule
		Method & Correctly classified & Root relative squared error\\
		\midrule
		NaiveBayes & $96.6449\%$ & $35.7222\%$\\
		NaiveBayes (10FCF) & $96.4352\%$ & $37.1926\%$\\
		J48 & $96.6449\%$ & $34.9136\%$\\
		J48 (10FCF) & $96.4352\%$ & $36.5122\%$\\
		\bottomrule
	\end{tabular}
	\caption{Results for \texttt{P1D} and \texttt{FD}}
\end{table}

\begin{table}[H]
	\centering
	\begin{tabular}{lllllll}
		\toprule
		\texttt{P2D} & \texttt{P1D} & \texttt{N2D} & \texttt{N1D} & \texttt{FW} & Correctly classified\\
		\midrule
		\checkmark{}	& \checkmark{}	& \checkmark{}	& \checkmark{}	& \checkmark{}	& $98.3225\%$\\
		\checkmark{}	& \checkmark{}	& \checkmark{}	& \checkmark{}	&	& $97.3905\%$\\
		\checkmark{}	& \checkmark{}	&	& \checkmark{}	& \checkmark{}	& $98.5555\%$\\
		\checkmark{}	& \checkmark{}	&	& \checkmark{}	&	& $97.507\%$\\
		\checkmark{}	& \checkmark{}	&	&	& \checkmark{}	& $98.0429\%$\\
		\checkmark{}	& \checkmark{}	&	&	&	& $95.5732\%$\\

			& \checkmark{}	& \checkmark{}	& \checkmark{}	& \checkmark{}	& $\%$\\
			& \checkmark{}	& \checkmark{}	& \checkmark{}	&	& $\%$\\
			& \checkmark{}	&	& \checkmark{}	& \checkmark{}	& $\%$\\
			& \checkmark{}	&	& \checkmark{}	&	& $\%$\\
			& \checkmark{}	&	&	& \checkmark{}	& $98.2992\%$\\
			& \checkmark{}	&	&	&	& $96.6449\%$\\

			&	& \checkmark{}	& \checkmark{}	& \checkmark{}	& $\%$\\
			&	& \checkmark{}	& \checkmark{}	&	& $\%$\\
			&	&	& \checkmark{}	& \checkmark{}	& $\%$\\
			&	&	& \checkmark{}	&	& $\%$\\
			&	&	&	& \checkmark{}	& $88.4436\%$\\

		\bottomrule
	\end{tabular}
	\caption{NaiveBayes}
\end{table}

\subsection*{Chapter 6: Exercises}
\begin{itemize}
	\item\emph{If we look at the Viterbi algorithm, we see that the
		probability of state at a given position is calculated $n$ the basis of
		the preceding $n$ states. However, it is claimed that the algorithm
		takes into account the whole sequence. Explain in your own words (at
		most $100$) how the probability is influenced by the rest of the
		sequence, i.e.\ both the positions more than n back and the following
		positions.}

	\item\emph{Explain in your own words (at most 50) how the EM algorithm
		works. I don't mean the mathematics, but the underlying concept.}
\end{itemize}
\end{document}
