fixes in a few chapters

plotting/lecture/plotting.tex

@@ -146,7 +146,7 @@ additional options consult the help.
 The following listing shows a simple line plot with axis labeling and a title
 
 \lstinputlisting[caption={A simple plot showing a sinewave.},
-  label=niceplotlisting]{simple_plot.m}
+  label=simpleplotlisting]{simple_plot.m}
 
 
 \subsection{Changing properties of a line plot}

programming/lecture/programming.tex

@@ -40,14 +40,12 @@ variable.
 \begin{figure}
   \centering
   \begin{subfigure}{.5\textwidth}
-    \includegraphics[width=0.8\textwidth]{variable}
-    \label{variable:a}
+    \includegraphics[width=0.8\textwidth]{variable}\label{variable:a}
   \end{subfigure}%
   \begin{subfigure}{.5\textwidth}
-    \includegraphics[width=.8\textwidth]{variableB}
-    \label{variable:b}
+    \includegraphics[width=.8\textwidth]{variableB}\label{variable:b}
   \end{subfigure}
-  \titlecaption{Variables} point to a memory
+  \titlecaption{Variables}{ point to a memory
     address. They further are described by their name and
     data type. The variable's value is stored as a pattern of binary
     values (0 or 1). When reading the variable this pattern is
@@ -630,7 +628,7 @@ matrix). The function \code{cat()} allows to concatenate n-dimensional
 matrices.
 
 To request the length of a vector we used the function
-\code{length()}. This function is \tetbf{not} suited to request
+\code{length()}. This function is \textbf{not} suited to request
 information about the size of a matrix. As mentioned above,
 \code{length()} would return the length of the largest dimension. The
 function \code{size()} however, returns the length in each dimension

programmingstyle/lecture/programmingstyle.tex

@@ -345,7 +345,7 @@ access (read or write) variables of the calling function. Interaction
 with the local function requires to pass all required arguments and to
 take care of the return values of the function.
 
-\emp{Nested functions} are different in this respect. They are
+\emph{Nested functions} are different in this respect. They are
 defined within the body of the parent function (between the keywords
 \code{function} and \code{end}) and have full access to all variables
 defined in the parent function. Working (in particular changing) the

regression/lecture/regression.tex

@@ -86,7 +86,7 @@ large deviations.
 $f_{cost}(\{(x_i, y_i)\}|\{y^{est}_i\})$ is a so called
 \enterm{objective function} or \enterm{cost function}. We aim to adapt
 the model parameters to minimize the error (mean square error) and
-thus the \emph{objective function}. In Chapter~\ref{maximumlikelihood}
+thus the \emph{objective function}. In Chapter~\ref{maximumlikelihoodchapter}
 we will show that the minimization of the mean square error is
 equivalent to maximizing the likelihood that the observations
 originate from the model (assuming a normal distribution of the data
@@ -270,7 +270,7 @@ The gradient is given by partial derivatives
 (Box~\ref{partialderivativebox}) with respect to the parameters $m$
 and $b$ of the linear equation. There is no need to calculate it
 analytically but it can be estimated from the partial derivatives
-using the difference quotient (Box~\ref{differentialquotient}) for
+using the difference quotient (Box~\ref{differentialquotientbox}) for
 small steps $\Delta m$ und $\Delta b$. For example the partial
 derivative with respect to $m$: