diff --git a/presentations/Bachelor-Short/LaTeX/bachelor-short.pdf b/presentations/Bachelor-Short/LaTeX/bachelor-short.pdf
new file mode 100644
index 0000000..188da91
Binary files /dev/null and b/presentations/Bachelor-Short/LaTeX/bachelor-short.pdf differ
diff --git a/presentations/Bachelor-Short/LaTeX/introduction.tex b/presentations/Bachelor-Short/LaTeX/introduction.tex
index da38362..9e7fcfa 100644
--- a/presentations/Bachelor-Short/LaTeX/introduction.tex
+++ b/presentations/Bachelor-Short/LaTeX/introduction.tex
@@ -24,6 +24,7 @@
     %     \item and provide data and a platform to test new ideas for classifiers
     % \end{itemize}
 
-    As soon as symbol recognition works good in terms of classification time and
-    performance, I will continue with formula recognition.
+    For now: recognition of isolated symbols. That means:
+
+    single symbol \enquote{formulae} rather than multi symbol formulae
 \end{frame}
\ No newline at end of file
diff --git a/presentations/Bachelor-Short/LaTeX/neural-nets.tex b/presentations/Bachelor-Short/LaTeX/neural-nets.tex
index 39b1ecc..e8fcfe6 100644
--- a/presentations/Bachelor-Short/LaTeX/neural-nets.tex
+++ b/presentations/Bachelor-Short/LaTeX/neural-nets.tex
@@ -3,13 +3,19 @@
     \textbf{Preprocessing:} Scaling, shifting and linear interpolation\\
     \textbf{Features:} Coordinates of 80 points (4 strokes with 20 points each)\\
     \textbf{Learning:} MLP, 300 epochs, LR of 0.1
-    \begin{itemize}
-        \item[] \textit{toplogy       \tabto{6cm} error in training time}
-        \item 160:500:369             \tabto{6cm} 30.62 \% in \hphantom{0}9min 08s
-        \item 160:500:500:369         \tabto{6cm} 27.73 \% in 11min 49s
-        \item 160:500:500:500:369     \tabto{6cm} 34.79 \% in 14min 09s
-        \item 160:500:500:500:500:369 \tabto{6cm} 33.61 \% in 14min 06s
-    \end{itemize}
+
+\begin{table}[h]
+    \begin{tabular}{lrl}
+    \toprule
+    Topology                & Error    & Training time \\ \midrule
+    160:500:369             & 30.62 \% & \hphantom{0}9min 08s      \\
+    160:500:500:369         & 27.73 \% & 11min 49s     \\
+    160:500:500:500:369     & 34.79 \% & 14min 09s     \\
+    160:500:500:500:500:369 & 33.61 \% & 14min 06s     \\
+    \bottomrule
+    \end{tabular}
+\end{table}
+
 \end{frame}
 
 \begin{frame}[fragile]{Examples of confusable symbols}
diff --git a/presentations/Bachelor-Short/LaTeX/will-do.tex b/presentations/Bachelor-Short/LaTeX/will-do.tex
index 12178e6..2b4f97a 100644
--- a/presentations/Bachelor-Short/LaTeX/will-do.tex
+++ b/presentations/Bachelor-Short/LaTeX/will-do.tex
@@ -3,7 +3,7 @@
     \begin{itemize}
         \item Evaluate preprocessing steps
         \item Try other features
-        \item Try other topologies / trainings (e.g. newbob)
+        \item Try other topologies / trainings (e.g. pretraining, newbob)
     \end{itemize}
 \end{frame}
 
diff --git a/presentations/Bachelor-Short/LaTeX/write-math.tex b/presentations/Bachelor-Short/LaTeX/write-math.tex
index 1479893..cab9397 100644
--- a/presentations/Bachelor-Short/LaTeX/write-math.tex
+++ b/presentations/Bachelor-Short/LaTeX/write-math.tex
@@ -2,12 +2,34 @@
 
 \begin{frame}{write-math.com}
     \begin{itemize}
-        \item a website where users can add labeled training data
+        \item a website where users can add labeled training data and unlabeled
+              data which they want to classify. I call this data \enquote{recording}
+        \begin{figure}[ht]
+            \centering
+            \subfloat{
+                \includegraphics[height=0.1\textwidth]{../images/279952.pdf}
+            }%
+            \qquad
+            \subfloat{
+                \includegraphics[height=0.1\textwidth]{../images/281507.pdf}
+            }%
+            \qquad
+            \subfloat{
+                \includegraphics[height=0.1\textwidth]{../images/287612.pdf}
+            }%
+            \qquad
+            \subfloat{
+                \includegraphics[height=0.1\textwidth]{../images/292175.pdf}
+            }%
+            \caption*{4 recordings}
+        \end{figure}
         \item works with desktop computers and touch devices
         \item symbol recognition can be done by multiple classifiers
-        \item users can contribute formulas
-        \item users can vote for formulas
-        \item user who wrote the formula can accept one formula
+        \item users can contribute formulas as recordings and as \LaTeX{} answers
+              for recordings
+        \item users can vote for \LaTeX{} answers:
+              \Large $\leq$, $\leqq$, $\leqslant$, \dots \normalsize
+        \item user who wrote the formula can accept one answer
     \end{itemize}
 \end{frame}
 
diff --git a/presentations/Bachelor-Short/templates/myStyle.sty b/presentations/Bachelor-Short/templates/myStyle.sty
index 691a180..9a4c862 100644
--- a/presentations/Bachelor-Short/templates/myStyle.sty
+++ b/presentations/Bachelor-Short/templates/myStyle.sty
@@ -19,7 +19,7 @@
 \usepackage{tikz}
 \usetikzlibrary{arrows,shapes}
 \usepackage{relsize}
-\usepackage{subfigure}
+\usepackage{subfig}
 \usepackage{algorithm,algpseudocode}
 \usepackage{minted}         % needed for the inclusion of source code
 \usepackage{menukeys}