diff --git a/rsc/Roc_curve.svg.png b/rsc/Roc_curve.svg.png
new file mode 100644
index 0000000..63c307d
Binary files /dev/null and b/rsc/Roc_curve.svg.png differ
diff --git a/src/experimentalresults.tex b/src/experimentalresults.tex
index 00ccabb..6c3b2cb 100644
--- a/src/experimentalresults.tex
+++ b/src/experimentalresults.tex
@@ -1,6 +1,7 @@
 \section{Experimental Results}
+\subsection{Does Active-Learning benefit the learning process?}
 
-\subsection{test}
+With the test setup described in~\ref{sec:implementation} a test series was performed.
 
 \begin{figure}
     \centering
@@ -14,8 +15,6 @@
     \caption{Architecture convolutional neural network}
 \end{figure}
 
-\subsection{test2}
-
 \begin{figure}
     \centering
     \includegraphics[width=\linewidth]{../rsc/AUC_normal_lowcer_2_10}
@@ -28,4 +27,7 @@
     \caption{Architecture convolutional neural network3}
 \end{figure}
 
-Test
\ No newline at end of file
+\subsection{Is Dagster and Label-Studio a proper tooling to build an AL
+Loop?}
+
+\subsection{Does balancing the learning samples improve performance?}
\ No newline at end of file
diff --git a/src/materialandmethods.tex b/src/materialandmethods.tex
index 3123b36..a8c5b28 100644
--- a/src/materialandmethods.tex
+++ b/src/materialandmethods.tex
@@ -27,6 +27,22 @@ Some of the labels are known, but for most of the data we have only the raw data
 The basic idea is that the unlabeled data can significantly improve the model performance when used in combination with the labeled data.
 
 \subsubsection{ROC and AUC}
+
+A receiver operating characteristic curve can be used to measure the performance of a classifier of a binary classification task.
+When using the accuracy as the performance metric it doesn't reveal much about the balance of the predictions.
+There might be many true-positives and rarely any true-negatives and the accuracy is still good.
+The ROC curve helps with this problem and visualizes the true-positives and false-positives on a line plot.
+The more the curve ascents the upper-left or bottom-right corner the better the classifier gets.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=\linewidth]{../rsc/Roc_curve.svg}
+    \caption{Architecture convolutional neural network. Image by \href{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}{SKY ENGINE AI}}
+    \label{fig:roc-example}
+\end{figure}
+
+Furthermore, the area under this curve is called AUR curve and a useful metric to measure the performance of a binary classifier.
+
 \subsubsection{RESNet}
 \subsubsection{CNN}
 Convolutional neural networks are especially good model architectures for processing images, speech and audio signals.