mlsem/slides.tex

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    \section{Organisation}

    \begin{frame}
        \frametitle{Today}
        \begin{columns}
            \begin{column}{.475\textwidth}
                \begin{itemize}
                    \item Kick-Off Meeting
                    \item Some Formalities
                    \item Short Overview of the Topics
                \end{itemize}
                \begin{center}
                    \includegraphics[height=4cm]{assigns.pdf}

                \end{center}
            \end{column}

            \begin{column}{.475\textwidth}
                \begin{itemize}
                    \item Choose a couple topics
                    \begin{itemize}
                        \item Since we are only a few, you can make these requests quite complicated (I prefer topic 1, but I would also take 3 or 7, except when I can do it in german, then I would prefer topic 12)
                    \end{itemize}
                    \item Send your choice to Simon.Kluettermann@cs.tu-dortmund.de (till tomorrow 20.10.2021 23:59)
                    \item You will be assigned one in the next days
                    \item Earlier submission will not be preferred, so take your time
                \end{itemize}

            \end{column}
        \end{columns}
    \end{frame}

    \begin{frame}
    \frametitle{Objective of this Seminar}
    
        \begin{itemize}

            \item Introduction to a wide range of machine learning
            \begin{itemize}

                \item Researching current scientific ideas
                \item Understanding state-of-the-art techniques
                \item Writing a clear and concisive scientific report
                \item Presenting and discussing your findings


            \end{itemize}

        \end{itemize}
        \rightarrow  Not a bad start for a final thesis.... \rightarrow maybe just talk to your supervisor about this

    \end{frame}

    \begin{frame}
    \frametitle{Timeline}
        \begin{enumerate}
            \item 3min Presentations in Class (November 2, 15:30-17:00)
            \item Presentation in Class (Weekly, Tuesdays 15:30-17:00, starting November 9)
            \item Discussion of your Topics (afterwards)
            \item Writing of your Report (till two weeks after your Presentation)
        \end{enumerate}
        \begin{itemize}
            \item All parts required!
        \end{itemize}
        \vspace{3em}
        \begin{itemize}
            \item Everything will be done in english. If this is a problem for you, please write Simon.Kluettermann@cs.tu.dortmund.de
        \end{itemize}
    \end{frame}

    \begin{frame}
    \frametitle{Tasks of this Seminar}
        %\begin{itemize}
        %    \item Required: willingless to learn something new
        %    \item Also useful: A basic understanding of machine learning, statistics and linear algebra
        %\end{itemize}
            \begin{enumerate}
                \item Choose a couple of topics from our list, you will be assigned to one of them
                \item Read and understand the chapters given to you
                \item Find, read and understand related literature. Try to get a wide picture of your Topic.
                    \begin{itemize}
                        \item You will often find alternative sources
                        \item Some Topics include a programming sections, but for all topics some programming can be useful.
                    \end{itemize}
            \end{enumerate}
        \begin{itemize}

            \item Final Results:
                \begin{itemize}
                    \item Presentation (30min +10min questions)
                    \item Written Report (at least 6 Pages double column, ACM template equivalent. Imagine yourself explaining your Topic to another student)
                \end{itemize}
        \end{itemize}


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    \section{Topics}

    \begin{frame}
    \frametitle{Topics}
        \begin{columns}
            \begin{column}{0.625\textwidth}
                \begin{itemize}
        \item Based on the Book "Neural Networks and Deep Learning"
        \item by C. Aggarwal, ISBN 978-3-319-94463-0
        \item Freely available trough University Network (Springer Link)

                \end{itemize}
                \begin{itemize}
        \item Some Topics contain programming assignments. We suggest using google colab for these.
                \end{itemize}

            \end{column}
            \begin{column}{0.325\textwidth}
                \begin{center}
                
                    \includegraphics[height=5cm]{illustrations/book.jpg}

                \end{center}

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    \end{columns}

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    \subfile{topics/w01.tex}
    \subfile{topics/w02.tex}
    \subfile{topics/w03.tex}
    \subfile{topics/w04.tex}
    \subfile{topics/w05.tex}
    \subfile{topics/w06.tex}
    \subfile{topics/w07.tex}
    \subfile{topics/w08.tex}
    \subfile{topics/w09.tex}
    \subfile{topics/w10.tex}

    %\begin{frame}
    %\begin{center}

%\begin{table}[]
%\begin{tabular}{|l|l|l|}
%\hline
%1: AD for Monitoring        & 2: High dim Data             & 3: Graph Data                   \\ \hline
%4: LOF                      & 5: Subspace prep             & 6: Transformations for AD       \\ \hline
%7: GANs                     & 8: Ensembles                 & 9: Active AD                    \\ \hline
%10: Interpretation          & 11: Explainability           & 12: Application: Device Failure \\ \hline
%13: Application: Video Data & 14: Application: Spacecrafts & Any other ideas?                \\ \hline
%\end{tabular}
%\end{table}

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\begin{frame}
    \begin{columns}
        \begin{column}{0.475\textwidth}
            \begin{itemize}
            \item Week 1: Intro to Ml *2
            \item Week 2: Backpropagation *2
            \item Week 3: Training techniques *2
            \item Week 4: Recurrent NN *2
            \item Week 5: Convolutions *2
            \end{itemize}
        \end{column}
        \begin{column}{0.475\textwidth}
            \begin{itemize}
            \item Week 6: Boltzmann Machines, Graph ML
            \item Week 7: Autoencoders *2
            \item Week 8: Reinforcement Learning *2
            \item Week 9: GANs, unsupervised ML
            \item Week 10: Attention Mechanisms and Competive Learning
            \end{itemize}
        
        \end{column}

    \end{columns}



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