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04-01-ACA-Inference-Classification.tex
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04-01-ACA-Inference-Classification.tex
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% move all configuration stuff into includes file so we can focus on the content
\input{include}
\subtitle{module 4.1: classification}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
% generate title page
\input{include/titlepage}
\section[overview]{lecture overview}
\begin{frame}{introduction}{overview}
\begin{block}{corresponding textbook section}
%\href{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331125}{Chapter 8: Musical Genre, Similarity, and Mood} (pp.~155)
section~4.1
\end{block}
\begin{itemize}
\item \textbf{lecture content}
\begin{itemize}
\item intuitive intro to machine learning
\item classifier examples
\end{itemize}
\bigskip
\item<2-> \textbf{learning objectives}
\begin{itemize}
\item describe the basic principles of data-driven machine learning approaches
\item implement a kNN classifier in Python
\end{itemize}
\end{itemize}
\inserticon{directions}
\end{frame}
\section[intro]{introduction}
\begin{frame}{classification}{introduction}
remember the flow chart of a general ACA system:
\vspace{-3mm}
\begin{figure}
\input{pict/introduction_ACASystem_3}
\end{figure}
\begin{itemize}
\item<2-> \color<3->{highlight}{\textit{classification}}:
\begin{itemize}
\item assign class labels to data
\end{itemize}
\item<2-> \textit{regression}:
\begin{itemize}
\item estimate numerical labels for data
\end{itemize}
\item<2-> \textit{clustering}:
\begin{itemize}
\item find grouping patterns in data
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}{classification}{basic example}
%\vspace{-3mm}
\begin{columns}
\column{.4\linewidth}
hypothetical system:
\begin{itemize}
\item one feature (envelope)
\item predefined threshold
\begin{itemize}
\item higher than threshold\\ $\Rightarrow$ class 1 (onset)
\item lower than threshold\\ $\Rightarrow$ class 0 (no onset)
\end{itemize}
\end{itemize}
\column{.6\linewidth}
\vspace{-10mm}
\figwithmatlab{ThresholdClassification}
\end{columns}
\end{frame}
\begin{frame}{classification}{data-driven}
\vspace{-3mm}
\begin{columns}
\column{.4\linewidth}
\begin{itemize}
\item derive classification parameters from data, e.g.,
\smallskip
\item[$\Rightarrow$] learn common feature distributions per class
\item[$\Rightarrow$] learn separation metrics per class
\end{itemize}
\column{.6\linewidth}
%\vspace{-10mm}
\figwithmatlab{FeatureSpace}
\end{columns}
\end{frame}
%\begin{frame}{classification}{general steps}
%\begin{enumerate}
%\item \textbf{define training set}: annotated results
%\smallskip
%\item<2-> \textbf{normalize} training set
%\smallskip
%\item<3-> \textbf{train} classifier
%\smallskip
%\item<4-> \textbf{evaluate} classifier with test (or validation) set
%\smallskip
%\item<5-> (\textbf{adjust} classifier settings, return to 4.)
%\end{enumerate}
%\end{frame}
%
%\section{classification}
%\begin{frame}{classification}{rules of thumb}
%\vspace{-3mm}
%\begin{itemize}
%\item \textbf{training set}
%\begin{itemize}
%\item training set size vs.\ number of features
%\begin{itemize}
%\item training set too small,% $\Rightarrow$ \textit{overfitting}
%feature number too large $\Rightarrow$ \textit{overfitting}
%\end{itemize}
%\item<1-> training set \textbf{too noisy} $\Rightarrow$ \textit{underfitting}
%\item<1-> training set \textbf{not representative} $\Rightarrow$ \textit{bad classification performance}
%\end{itemize}
%\smallskip
%\item<2-> \textbf{classifier}
%\begin{itemize}
%\item<2-> classifier too complex $\Rightarrow$ \textit{overfitting}
%\item<2-> \textbf{poor classifier} $\Rightarrow$ \textit{bad classification performance}
%\begin{itemize}
%\item[$\rightarrow$] different classifier
%\end{itemize}
%\end{itemize}
%\smallskip
%\item<3-> \textbf{features}
%\begin{itemize}
%\item<3-> \textbf{poor features} $\Rightarrow$ \textit{bad classification performance}
%\begin{itemize}
%\item[$\rightarrow$] new, better features
%\end{itemize}
%\item<3-> features \textbf{not normalized} $\Rightarrow$ possibly \textit{bad classification performance}
%\begin{itemize}
%\item feature distribution (range, mean, symmetry)
%\end{itemize}
%\end{itemize}
%\end{itemize}
%\end{frame}
%\begin{frame}{classification}{evaluation}
%\begin{itemize}
%\item define \textbf{test set} for evaluation
%\begin{itemize}
%\item test set \textit{different} from training set
%\item otherwise, same requirements
%\end{itemize}
%
%\bigskip
%\item<2-> example: \textbf{$N$-fold cross validation}
%\begin{enumerate}
%\item<2-> split training set into $N$ parts (randomly, but preferably identical number per class)
%\item<3-> select one part as test set
%\item<4-> train the classifier with all observations from remaining $N-1$ parts
%\item<5-> compute the classification rate for the test set
%\item<6-> repeat until all $N$ parts have been tested
%\item<7-> overall result: \textit{average} classification rate
%\end{enumerate}
%\end{itemize}
%\end{frame}
%
\section[examples]{classifier examples}
\begin{frame}{classifier examples}{k-Nearest Neighbor (kNN)}
\vspace{-5mm}
\begin{columns}
\column{.6\linewidth}
\begin{itemize}
\item \textbf{training}: extract reference vectors from training set
\begin{itemize}
\item store coordinates and class labels
\end{itemize}
\bigskip
\item<2-> \textbf{classification}: extract query vector and set class to majority of $k$ nearest reference vectors
\begin{enumerate}
\item compute distance between query and all training vectors
\item sort distances to find closest vectors
\item choose majority class out of the $k$ closest vectors
\end{enumerate}
\bigskip
\item<3-> \textbf{classifier model}: all training vectors
\end{itemize}
\column{.4\linewidth}
%\only<1,6>{\begin{figure}\includegraphics[width=\columnwidth]{Knn-0}\end{figure}}
%\setcounter{i}{1}
%\setcounter{j}{2}
%\whiledo{\value{i}<5}
%{
%\only<\value{j}>{\begin{figure}\includegraphics[width=\columnwidth]{Knn-\arabic{i}}\end{figure}}
%\stepcounter{i}
%\stepcounter{j}
%}
\figwithmatlab{Knn}
\only<4>{$k = 3 \Rightarrow$ blue majority}
\only<5>{$k = 5 \Rightarrow$ black majority}
\only<6>{$k = 7 \Rightarrow$ black majority}
\end{columns}
\end{frame}
\begin{frame}{classifier examples}{Gaussian Mixture Model (GMM)}
\vspace{-5mm}
\begin{columns}
\column{.4\linewidth}
\begin{itemize}
\item \textbf{training}:\\ model each class distribution as superposition of Gaussian distributions
\bigskip
\item<2-> \textbf{classification}:\\ compute output of each Gaussian and select class with highest probability
\bigskip
\item<3-> \textbf{classifier data}:\\ per class per Gaussian: $\mu$ and covariance, mixture weight
\end{itemize}
\column{.6\linewidth}
\vspace{-11mm}
\figwithmatlab{Gmm}
\end{columns}
\end{frame}
\begin{frame}{classifier examples}{Support Vector Machine (SVM)}
\begin{itemize}
\item \textbf{training}:
\begin{itemize}
\item map features to high dimensional space
\figwithref{graph/SVM}{\href{https://en.wikipedia.org/wiki/Support\_vector\_machine}{https://en.wikipedia.org/wiki/Support\_vector\_machine}}
\item find separating hyperplane through maximum distance of support vectors (data points)
\end{itemize}
\item<2-> \textbf{classification}: apply feature transform and proceed with 'linear' classification
\item<3-> \textbf{classifier data}: support vectors, kernel, kernel parameters
\end{itemize}
\end{frame}
\section{summary}
\begin{frame}{summary}{lecture content}
\begin{itemize}
\item \textbf{data-driven approach}
\begin{itemize}
\item 'general' system learns parameters/behavior from data
\item human interaction through
\begin{itemize}
\item parametrization and procedures
\item data selection
\end{itemize}
\end{itemize}
\bigskip
\item \textbf{many classifiers with different levels of complexity}
\begin{enumerate}
\item kNN
\item GMM
\item SVM
\item RandomForest
\item DNN
\item \ldots
\end{enumerate}
%\bigskip
%\item \textbf{fine balance of inputs}
%\begin{enumerate}
%\item number of features
%\item classifier complexity
%\item amount and variability of data
%\end{enumerate}
\end{itemize}
\inserticon{summary}
\end{frame}
\end{document}