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\title[Thesis Simon]{Open Thesis Topics}   
\author{Simon.Kluettermann@cs.tu-dortmund.de} 
\date{\today} 


 \institute{ls9 tu Dortmund}


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\begin{document}



%from file ../knn1//data/000.txt
\begin{frame}[label=]
\frametitle{}
\begin{titlepage}

	\centering
	{\huge\bfseries \par}
	\vspace{2cm}
	{\LARGE\itshape Simon Kluettermann\par}
	\vspace{1.5cm}
	{\scshape\Large Master Thesis in Physics\par}
	\vspace{0.2cm}
	{\Large submitted to the \par}
	\vspace{0.2cm}
	{\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par}
	\vspace{0.2cm}
	{\Large \par}
	\vspace{0.2cm}
	{\scshape\Large RWTH Aachen University}
	\vspace{1cm}
	
	\vfill
	{\scshape\Large Department of Physics\par}
	\vspace{0.2cm}
	{\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par}
	\vspace{0.2cm}
	{ \Large\par}
	\vspace{0.2cm}
	{\Large First Referee: Prof. Dr. Michael Kraemer \par}
	{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}

	\vfill

% Bottom of the page
	{\large November 2020 \par}
\end{titlepage}
\pagenumbering{roman}
\thispagestyle{empty}
\null
\newpage
\setcounter{page}{1}
\pagenumbering{arabic}
\end{frame}


%from file ../knn1//data/001Thesis@ls9.txt
\begin{frame}[label=Thesis@ls9]
\frametitle{Thesis@ls9}
\begin{itemize}

    \item First: Find a topic and a supervisor

    \item Work one month on this, to make sure

\begin{itemize}

    \item you still like your topic

    \item and you are sure you can handle the topic


\end{itemize}
    \item then short presentation in front of our chair (15min, relaxed)

\begin{itemize}

    \item get some feedback/suggestions


\end{itemize}
    \item afterwards register the thesis

\begin{itemize}

    \item (different for CS/DS students)


\end{itemize}
    \item Problem: We are not able to supervise more than 2 students at the same time (CS faculty rules)


\end{itemize}
\end{frame}


%from file ../knn1//data/002Today.txt
\begin{frame}[label=Today]
\frametitle{Today}
\begin{itemize}

    \item First: A short summary of each Topic

    \item Then time for questions/Talk with your supervisor about each topic that sounds interesting

    \item Your own topics are always welcome;)


\end{itemize}
\end{frame}


%from file ../knn1//data/003Anomaly Detection.txt
\begin{frame}[label=Anomaly Detection]
\frametitle{Anomaly Detection}
\begin{columns}[c] % align columns
\begin{column}{0.48\textwidth}%.48
\begin{itemize}

    \item Im working on Anomaly Detection

    \item That means characterising an often very complex distributions, to find events that dont match the expected distribution


\end{itemize}
\end{column}%
\hfill%
    \begin{column}{0.48\textwidth}%.48
\begin{figure}[H] 
  \centering
\includegraphics[width=0.9\textwidth]{../prep/03Anomaly_Detection/circle.pdf}
\label{fig:prep03Anomaly_Detectioncirclepdf}
  \end{figure}


\end{column}%
\hfill%
\end{columns}

\end{frame}


%from file ../knn1//data/004knn.txt
\begin{frame}[label=knn]
\frametitle{knn}
\begin{itemize}

    \item kNN algorithm can also be used for AD

    \item if the k closest point is further away, a sample is considered more anomalous

    \item $r=\frac{k}{2N\cdot pdf}$

    \item Powerful method, as it can model the pdf directly


\end{itemize}
\end{frame}


%from file ../knn1//data/005Better knn.txt
\begin{frame}[label=Better knn]
\frametitle{Better knn}
\begin{itemize}

    \item The model (mostly) ignores every known sample except one

    \item So there are extensions

    \item $avg=\frac{1}{N} \sum_i knn_i(x)$

    \item $wavg=\frac{1}{N} \sum_i \frac{knn_i(x)}{i}$


\end{itemize}
\end{frame}


%from file ../knn1//data/006Comparison.txt
\begin{frame}[label=Comparison]
%\frametitle{Comparison}


\begin{tabular}{llllll}
\hline
 Dataset                   & wavg                          & avg                           & 1                   & 3                   & 5                   \\
\hline
 $vertebral$               & $\textbf{0.4506}$             & $\textbf{0.4506}$             & $\textbf{0.4667}$   & $\textbf{0.4667}$   & $\textbf{0.45}$     \\
    ... & & & & & \\
 $thyroid$                 & $\textbf{0.9138}$             & $\textbf{0.9151}$             & $\textbf{0.8763}$   & $\textbf{0.9086}$   & $\textbf{0.914}$    \\
 $Iris\_setosa$            & $\textbf{0.9333}$             & $\textbf{0.9333}$             & $\textbf{0.9333}$   & $\textbf{0.9}$      & $\textbf{0.9}$      \\
 $breastw$                 & $\textbf{0.9361}$             & $\textbf{0.9361}$             & $\textbf{0.9211}$   & $\textbf{0.9248}$   & $\textbf{0.9286}$   \\
 $wine$                    & $\textbf{0.95}$               & $\textbf{0.95}$               & $\textbf{0.9}$      & $\textbf{0.95}$     & $\textbf{0.95}$     \\
 $pendigits$               & $\textbf{0.9487}$             & $\textbf{0.9487}$             & $\textbf{0.9391}$   & $\textbf{0.9295}$   & $\textbf{0.9359}$   \\
 $segment$                 & $\textbf{0.9747}$             & $\textbf{0.9747}$             & $\textbf{0.9495}$   & $\textbf{0.9545}$   & $\textbf{0.9394}$   \\
 $banknote-authentication$ & $\textbf{0.9777}$             & $\textbf{0.9776}$             & $\textbf{0.9408}$   & $\textbf{0.943}$    & $\textbf{0.9583}$   \\
 $vowels$                  & $\textbf{0.9998}$             & $\textbf{0.9972}$             & $\textbf{0.99}$     & $\textbf{0.92}$     & $\textbf{0.93}$     \\
 $Ecoli$                   & $\textbf{1.0}$                & $\textbf{1.0}$                & $\textbf{0.9}$      & $\textbf{1.0}$      & $\textbf{1.0}$      \\
 $$                        & $$                            & $$                            & $$                  & $$                  & $$                  \\
 $Average$                 & $\textbf{0.7528} $ & $\textbf{0.7520}  $ & $0.7325 $ & $0.7229 $ & $0.7157 $ \\
\hline
\end{tabular}



\end{frame}


%from file ../knn1//data/007What to do?.txt
\begin{frame}[label=What to do?]
\frametitle{What to do?}
\begin{itemize}

    \item Evaluation as anomaly detector is complicated

\begin{itemize}

    \item Requires known anomalies


\end{itemize}
    \item $\Rightarrow$So evaluate as density estimator

\begin{itemize}

    \item Does not require anomalies

    \item Allows generating infinite amounts of training data


\end{itemize}

\end{itemize}
\end{frame}


%from file ../knn1//data/008What to do?.txt
\begin{frame}[label=What to do?]
\frametitle{What to do?}
\begin{itemize}

    \item Collect Extensions of the oc-knn algorithm

    \item Define some distance measure to a known pdf

    \item Generate random datapoints following the pdf

    \item Evaluate which algorithm finds the pdf the best


\end{itemize}
\end{frame}


%from file ../knn1//data/009Requirements.txt
\begin{frame}[label=Requirements]
\frametitle{Requirements}
\begin{itemize}

    \item Knowledge of python ( sum([i for i in range(5) if i\%2]) )

\begin{itemize}

    \item Ideally incl numpy


\end{itemize}
    \item Basic university level Math (you could argue that $r_k \propto \frac{k}{pdf}$)

    \item Ideally some experience working on a ssh server

    \item $\Rightarrow$Good as a Bachelor Thesis

    \item For a Master Thesis, I would extend this a bit (Could you also find $k$?)


\end{itemize}
\end{frame}


%from file ../knn1//data/010Normalising Flows.txt
\begin{frame}[label=Normalising Flows]
\frametitle{Normalising Flows}
\begin{itemize}

    \item Deep Learning Method, in which the output is normalised

    \item $\int f(x) dx=1 \; \forall f(x)$

    \item Can be used to estimate probability density functions

    \item $\Rightarrow$Thus useful for AD

    \item $\int f(h(x)) \|\frac{\delta x}{\delta h}\| dh=1 \; \forall h(x)$


\end{itemize}
\end{frame}


%from file ../knn1//data/011Graph Normalising Flows.txt
\begin{frame}[label=Graph Normalising Flows]
\frametitle{Graph Normalising Flows}
\begin{itemize}

    \item How to apply this to graphs?

    \item One Paper (Liu 2019) uses two NN:

    \item Autoencoder graph$\Rightarrow$vector

    \item NF on vector data

    \item which is fine, but also not really graph specific

    \item No interaction between encoding and transformation


\end{itemize}
\end{frame}


%from file ../knn1//data/012Graph Normalising Flows.txt
\begin{frame}[label=Graph Normalising Flows]
\frametitle{Graph Normalising Flows}
\begin{itemize}

    \item So why not do this directly?

    \item $\Rightarrow$Requires differentiating a graph

    \item Why not use only one Network?

    \item Graph$\Rightarrow$Vector$\Rightarrow$pdf

    \item $\Rightarrow$Finds trivial solution, as $<pdf> \propto \frac{1}{\sigma_{Vector}}$

    \item So regularise the standart deviation of the vector space!

\begin{itemize}

    \item Interplay between encoding and NF

    \item Could also be useful for highdim data


\end{itemize}

\end{itemize}
\end{frame}


%from file ../knn1//data/013Requirements.txt
\begin{frame}[label=Requirements]
\frametitle{Requirements}
\begin{itemize}

    \item Proficient in python ( [i for i in range(1,N) if not [j for j in range(2,i) if not i\%j]] )

\begin{itemize}

    \item Ideally incl numpy, tensorflow, keras


\end{itemize}
    \item Some deep learning experience

    \item University level math (google Cholesky Decomposition. Why is this useful for NF?)

    \item Ideally some experience working on a ssh server

    \item A bit more challenging$\Rightarrow$Better as a Master thesis

    \item (Still we would start very slowly of course)


\end{itemize}
\end{frame}


%from file ../knn1//data/014Old Thesis Sina.txt
\begin{frame}[label=Old Thesis Sina]
\frametitle{Sina}
\begin{columns}[c] % align columns
\begin{column}{0.48\textwidth}%.48
\begin{itemize}

    \item Isolation Forest: Different Anomaly Detection Algorithm

    \item Problem: Isolation Forests dont work on categorical data

    \item $\Rightarrow$Extend them to categorical data


\end{itemize}
\end{column}%
\hfill%
    \begin{column}{0.48\textwidth}%.48
\begin{figure}[H] 
  \centering
\includegraphics[width=0.9\textwidth]{../prep/20Old_Thesis_Sina/Bildschirmfoto vom 2022-09-26 16-22-30.png}
\label{fig:prep20Old_Thesis_SinaBildschirmfoto vom 2022-09-26 16-22-30png}
  \end{figure}


\end{column}%
\hfill%
\end{columns}

\end{frame}


%from file ../knn1//data/015Old Thesis Britta.txt
\begin{frame}[label=Old Thesis Britta]
\frametitle{Britta}
\begin{columns}[c] % align columns
\begin{column}{0.58\textwidth}%.48
\begin{itemize}

    \item Reidentification: Find known objects in new images

    \item Task: Find if two images of pallet blocks are of the same pallet block

    \item Use AD to represent the pallet blocks


\end{itemize}
\end{column}%
\hfill%
    \begin{column}{0.38\textwidth}%.48
\begin{figure}[H] 
  \centering
\includegraphics[width=0.9\textwidth]{../prep/21Old_Thesis_Britta/Bildschirmfoto vom 2022-09-26 16-23-26.png}
\label{fig:prep21Old_Thesis_BrittaBildschirmfoto vom 2022-09-26 16-23-26png}
  \end{figure}


\end{column}%
\hfill%
\end{columns}

\end{frame}


%from file ../knn1//data/016Old Thesis Hsin Ping.txt
\begin{frame}[label=Old Thesis Hsin Ping]
\frametitle{Hsin Ping}
\begin{columns}[c] % align columns
\begin{column}{0.48\textwidth}%.48
\begin{itemize}

    \item Ensemble: Combination of multiple models

    \item Task: Explain the prediction of a model using the ensemble structure


\end{itemize}
\end{column}%
\hfill%
    \begin{column}{0.48\textwidth}%.48
\begin{figure}[H] 
  \centering
\includegraphics[width=0.9\textwidth]{../prep/22Old_Thesis_Hsin_Ping/Bildschirmfoto vom 2022-09-26 16-24-14.png}
\label{fig:prep22Old_Thesis_Hsin_PingBildschirmfoto vom 2022-09-26 16-24-14png}
  \end{figure}


\end{column}%
\hfill%
\end{columns}

\end{frame}


%from file ../knn1//data/017Old Thesis Nikitha.txt
\begin{frame}[label=Old Thesis Nikitha]
\frametitle{Nikitha}
\begin{columns}[c] % align columns
\begin{column}{0.48\textwidth}%.48
\begin{itemize}

    \item Task: Explore a new kind of ensemble

    \item Instead of many uncorrelated models, let the models interact during training


\end{itemize}
\end{column}%
\hfill%
    \begin{column}{0.48\textwidth}%.48
\begin{figure}[H] 
  \centering
\includegraphics[width=0.9\textwidth]{../prep/23Old_Thesis_Nikitha/Bildschirmfoto vom 2022-09-26 16-25-06.png}
\label{fig:prep23Old_Thesis_NikithaBildschirmfoto vom 2022-09-26 16-25-06png}
  \end{figure}


\end{column}%
\hfill%
\end{columns}

\end{frame}

\begin{frame}
Questions?
\end{frame}



\end{document}
