add some math formulation of label set selection

src/main.tex

@@ -75,19 +75,6 @@
     \input{experimentalresults}
     \input{conclusionandoutlook}
 
-    \section{Semi-Supervised learning}\label{sec:semi-supervised-learning}
-    In traditional supervised learning we have a labeled dataset.
-    Each datapoint is associated with a corresponding target label.
-    The goal is to fit a model to predict the labels from datapoints.
-
-    In traditional unsupervised learning there are also datapoints but no labels are known.
-    The goal is to find patterns or structures in the data.
-    Moreover, it can be used for clustering or downprojection.
-
-    Those two techniques combined yield semi-supervised learning.
-    Some of the labels are known, but for most of the data we have only the raw datapoints.
-    The basic idea is that the unlabeled data can significantly improve the model performance when used in combination with the labeled data.
-
     \section{FixMatch}\label{sec:fixmatch}
     There is an already existing approach called FixMatch.
     This was introduced in a Google Research paper from 2020~\cite{fixmatch}.