From d11f5859ee7755b293468f2533b4f5d56e1c4b6b Mon Sep 17 00:00:00 2001
From: Mart Lubbers <mart@martlubbers.net>
Date: Tue, 13 Sep 2016 09:24:03 +0200
Subject: [PATCH] update

---
 week3/Makefile |  3 ++-
 week3/eml.tex  | 21 ++++++++++++++++-----
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/week3/Makefile b/week3/Makefile
index feeddb6..b7e1f69 100644
--- a/week3/Makefile
+++ b/week3/Makefile
@@ -5,7 +5,8 @@ PDFLATEX:=pdflatex $(PDFLATEXFLAGS)
 all: $(addsuffix .pdf,$(DOCUMENTS))
 
 %.pdf: %.tex
-	pdflatex $<
+	$(PDFLATEX) $<
+	$(PDFLATEX) $<
 
 clean:
 	$(RM) -v $(addprefix $(DOCUMENTS),.pdf .log .aux)
diff --git a/week3/eml.tex b/week3/eml.tex
index 781f2fe..e44cd40 100644
--- a/week3/eml.tex
+++ b/week3/eml.tex
@@ -29,10 +29,10 @@ Using ten-fold cross validation decreases the percentage.
 		\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) & $96.4352\%$ & $36.5122\%$\\
+		\emph{NaiveBayes} & $96.6449\%$ & $35.7222\%$\\
+		\emph{NaiveBayes (10FCF)} & $96.4352\%$ & $37.1926\%$\\
+		\emph{J48} & $96.6449\%$ & $34.9136\%$\\
+		\emph{J48 (10FCF)} & $96.4352\%$ & $36.5122\%$\\
 		\bottomrule
 	\end{tabular}
 	\caption{Results for \texttt{P1D} and \texttt{FD}\label{t1}}
@@ -65,7 +65,7 @@ Using ten-fold cross validation decreases the percentage.
 		& & & & \checkmark{} & $88.4436\%$\\
 		\bottomrule
 	\end{tabular}
-	\caption{NaiveBayes on all sensible combinations\label{t2}}
+	\caption{\emph{NaiveBayes} on all sensible combinations\label{t2}}
 \end{table}
 
 \subsection*{Chapter 6: Exercises}
@@ -78,8 +78,19 @@ Using ten-fold cross validation decreases the percentage.
 		sequence, i.e.\ both the positions more than $n$ back and the following
 		positions.}
 
+		A probability is always based on the most probably preceding sequence,
+		however there are no backpointers to all states. Thus the probability
+		is not based on all possible previous paths and you can only recover
+		the most likely path. The following states are also of an influence
+		since when the path does not belong to the most likely sequence it will
+		not be connected to the final path via a backpointer and will be lost.
+
 	\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.}
 
+		The \emph{Expectation-Maximization}-algorithm (EM) searches for the
+		settings of parameters where the likelihood is (locally) optimal. 
+		The algorithm usually takes the derivative of the likelihood function
+		to get the maximum value.
 \end{itemize}
 \end{document}
-- 
2.20.1