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\UseRawInputEncoding
%\documentclass[hyperref={pdfpagelabels=false}]{beamer}
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\title[Anomaly Detection and AutoML]{Lets go deeper!}
\author{Simon Kluettermann}
\date{\today}
\institute{ls9 tu Dortmund}
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\begin{document}
%from file ../case3/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 ../case3/data/001reminder.txt
\begin{frame}[label=reminder]
\frametitle{reminder}
\begin{itemize}
\item No Case Study next week
\begin{itemize}
\item neither Tuesday (29.11) nor Thursday (01.12)
\item if you need help: just write me an email!
\end{itemize}
\item In two weeks: Case Study switched
\begin{itemize}
\item Q+A Tuesday (6.12, 14:00) online only
\item Case Study Meeting Thursday (08.12, 14:00-16:00), in OH12 Room 3.032
\end{itemize}
\end{itemize}
\end{frame}
%from file ../case3/data/002Big Picture.txt
\begin{frame}[label=Big Picture]
\frametitle{Big Picture}
\begin{itemize}
\item Goal for this case study: Have better hyperparameters than pyod!
\item So each of you: Gets assigned two algorithms!
\begin{itemize}
\item One fairly simple before
\item One more complicated one today
\end{itemize}
\item Try to find the best possible hyperparameters for your algorithms
\begin{itemize}
\item Try to be clever (for example: PCA: $n_{components}<n_{features}$. Maybe $\frac{n_{components}}{n_{features}}$ constant?
\end{itemize}
\item Afterwards
\begin{itemize}
\item Write down your findings into a simple function (given data, what are my best hyperparameters)
\item Write down your finding into a report (together, double collumn. max 6 Pages per student, plus comparison of algorithms to each other)
\item One final presentation together in front of my colleagues. About 10min per student.
\end{itemize}
\end{itemize}
\end{frame}
%from file ../case3/data/003Evaluating your hyperparameter.txt
\begin{frame}[label=Evaluating your hyperparameter]
\frametitle{Evaluating your hyperparameter}
\begin{itemize}
\item My suggestion: Compare to normal parameters.
\item This means you get two lists of AUC scores
\item Your params: [0.80,0.75,0.73,....,0.95]
\item Pyod params: [0.82,0.71,0.48,....,0.95]
\item look at two values
\item $\sum_i your_i-pyod_i$
\begin{itemize}
\item Total improvment. If positive, then your parameters help;)
\item But hard to see if this is significant
\end{itemize}
\item Fraction of $your_i>pyod_i$
\begin{itemize}
\item Quantised, so does not care about improving your parameters further
\item But easy to see if this is significant
\begin{itemize}
\item 0.5$\Rightarrow$Probably just random
\item 0.9$\Rightarrow$Probably quite significant
\end{itemize}
\end{itemize}
\end{itemize}
\end{frame}
%from file ../case3/data/004How to continue.txt
\begin{frame}[label=How to continue]
\frametitle{How to continue}
\begin{itemize}
\item See how far you can improve this?
\item Treat this as a supervised optimisation problem: Given this dataset, find the best hyperparameters
\item Might be useful to look at more input parameters
\item Might help to formulate your parameters differently
\item But be aware of \textbf{overfitting}!
\end{itemize}
\end{frame}
%from file ../case3/data/005Intro to Deep Learning.txt
\begin{frame}[label=Intro to Deep Learning]
\frametitle{Intro to Deep Learning}
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{..//prep/05Intro_to_Deep_Learning/adsasda.png}
\label{fig:prep05Intro_to_Deep_Learningadsasdapng}
\end{figure}
\end{frame}
%from file ../case3/data/006Intro to Deep Learning.txt
\begin{frame}[label=Intro to Deep Learning]
\frametitle{Intro to Deep Learning}
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{..//prep/06Intro_to_Deep_Learning/adsasd.png}
\label{fig:prep06Intro_to_Deep_Learningadsasdpng}
\end{figure}
\end{frame}
%from file ../case3/data/007Intro to Deep Learning.txt
\begin{frame}[label=Intro to Deep Learning]
\frametitle{Intro to Deep Learning}
\begin{itemize}
\item The idea is always the same:
\begin{itemize}
\item Define complicated model to learn (often millions of parameters)
\item Define loss function that this model should minimize (example: $\sum_i (y_i-f(x_i))^2$)
\item Find parameters that minimize the loss ($\Rightarrow$Backpropagation)
\end{itemize}
\item Usually Neural Networks:
\begin{itemize}
\item $f(x)=f_n(x)=activation(A_n\cdot f_{n-1}(x)+b_n)$
\item $f_0(x)=x$
\end{itemize}
\item Powerful, as you can show that when there are 3 Layers+ (and infinitely sized matrices), you can approximate any function
\item $\Rightarrow$So a model becomes a loss function
\end{itemize}
\end{frame}
%from file ../case3/data/008Autoencoder.txt
\begin{frame}[label=Autoencoder]
\frametitle{Autoencoder}
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{..//prep/08Autoencoder/ae.png}
\label{fig:prep08Autoencoderaepng}
\end{figure}
\end{frame}
%from file ../case3/data/009Autoencoder.txt
\begin{frame}[label=Autoencoder]
\frametitle{Autoencoder}
\begin{itemize}
\item Lets look at some of its Hyperparameters
\item Autoencoder Specific
\begin{itemize}
\item Compression factor (Latent space size)
\item Loss function (mse?)
\end{itemize}
\item Neural Network architecture
\begin{itemize}
\item Number of layers
\item Number of neurons in each layer (Shape of the matrices $A_n$)
\end{itemize}
\item Optimisation parameters
\begin{itemize}
\item Learning Rate
\begin{itemize}
\item Controls how fast the parameters are found
\item To high value makes the training unstable
\end{itemize}
\item Batch size
\begin{itemize}
\item Controls how many samples are averaged together.
\item Lower values make the training more stable, but also the result less optimal
\end{itemize}
\end{itemize}
\end{itemize}
\end{frame}
%from file ../case3/data/010For next time.txt
\begin{frame}[label=For next time]
\frametitle{For next time}
\begin{itemize}
\item (if you have not finished finding good parameters for your old algorithm, continue searching for them)
\item Take a look at your new algorithm
\item Run it once on cardio, take a look at which parameters you have
\item Prepare a similar presentation to last time (include your cardio result)
\end{itemize}
\end{frame}
\end{document}
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