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PCA, Online-PCA
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\section{Introduction} | ||
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\definecolor{darkgreen}{rgb}{0,0.6,0} | ||
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\mode<presentation>{ | ||
\begin{frame} | ||
\only<1,2>{ | ||
\frametitle{What \underline{not} to expect from this course}} | ||
\only<3->{\frametitle{Then what \underline{can} we expect from this course?}} | ||
%\vspace{10mm} | ||
\only<2>{ | ||
\begin{center} | ||
%Not your typical data science course | ||
\includegraphics[width=5cm]{img/meme_mi2} | ||
\end{center} | ||
} | ||
\only<3->{ | ||
\begin{center} | ||
breadth of topics, not necessarily state of the art\\ | ||
theoretical aspects of the learning algorithms | ||
\end{center} | ||
} | ||
\only<4->{ | ||
\begin{center} | ||
implementing the algorithms - programming exercises. | ||
\end{center} | ||
} | ||
\only<5>{ | ||
\begin{center} | ||
%Not your typical data science course | ||
\includegraphics[width=5.5cm]{img/meme_programming} | ||
\end{center} | ||
} | ||
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\end{frame} | ||
} | ||
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\subsection{Learning Paradigms} | ||
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\subsubsection{Supervised learning} | ||
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\begin{frame}{\subsecname:~\subsubsecname} | ||
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We are given data that is made up of tuples. | ||
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Supervised methods try to fit a function that maps $\vec x$ to some attribute $\vec y$ (labels/ground truth). | ||
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\begin{center} | ||
\includegraphics[height=3cm]{img/tigerente} | ||
\captionof*{figure}{example image recognition} | ||
\end{center} | ||
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Supervised learning would fit a function that maps image pixels to recognizing the object in the image\\ | ||
from a set of possible objects (e.g. cat, dog, tiger, duck, frog, bird). | ||
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\end{frame} | ||
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\subsubsection{Unsupervised learning} | ||
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\begin{frame}{\subsecname: \subsubsecname} | ||
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We are given data that is made up of observations\pause~\textbf{only}. No other information. | ||
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\mode<presentation>{ | ||
\begin{center} | ||
\includegraphics[width=4cm]{img/meme_nolabels} | ||
\end{center} | ||
} | ||
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\end{frame} | ||
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\begin{frame}{\subsubsecname} | ||
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\svspace{-5mm} | ||
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\question{What can we do when we only have observations?} | ||
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\pause | ||
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\mode<presentation>{ | ||
\only<2>{ | ||
\vspace{-5mm} | ||
\begin{center} | ||
\includegraphics[width=5cm]{img/meme_ai} | ||
\end{center} | ||
} | ||
} | ||
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\pause | ||
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\mode<article> | ||
Unsupervised learning tries to | ||
\mode<all> | ||
find interesting directions and/or structure in the data using only observations $\vec x \in \R^N$. | ||
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\begin{center} | ||
\includegraphics[width=9cm]{img/mi2} | ||
\end{center} | ||
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``interesting'' and ``structure'' is expressed through the objective model. | ||
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\end{frame} | ||
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\begin{frame}{\subsubsecname} | ||
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\underline{Data}: | ||
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A dataset of observations: | ||
\begin{equation} | ||
\label{eq:observations} | ||
\vec X = | ||
\left( | ||
\begin{array}{cccccc} | ||
\Big| & \Big| & & \Big| & & \Big| \\[3mm] | ||
\vec x^{(1)} & \vec x^{(2)} & \cdots & \vec x^{(\alpha)} & \cdots & \vec x^{(p)}\\[2mm] | ||
\Big| & \Big| & & \Big| & & \Big| | ||
\end{array} | ||
\right) \in \R^{N \times p} | ||
\end{equation} | ||
\notesonly{ | ||
where $p$ denotes the number of observations (i.e. size of the dataset) and $N$ denotes the number of dimensions.} | ||
\slidesonly{ | ||
where | ||
\begin{itemize} | ||
\item[] $p$ \corresponds\, no. of observations (often \iid) | ||
\item[] $N$ \corresponds\, no. of dimensions | ||
\end{itemize} | ||
} | ||
\notesonly{ | ||
The samples are often assumed to be \iid, but algorithms for handling sequential data also exist. | ||
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Example: $\vec x$ could represent user ratings, pixel values in images. | ||
} | ||
\end{frame} | ||
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\begin{frame}{Objective model} | ||
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%\svspace{-5mm} | ||
\mode<article>{ | ||
\underline{Objective model}: | ||
An unsupervised learning algorithm is used to capture structure or directions in the data. This can be achieved by finding: | ||
} | ||
\begin{itemize} | ||
\item the underlying distribution $P(\vec x)$ that generated this data (e.g. density estimation), | ||
\item $\vec u := \vec f(\vec x)$, where $\vec u$ is a measure of | ||
\begin{itemize} | ||
\item possible structure such as clustering or grouping in the data ($\vec u \in {0,\ldots,K-1}$), \\ | ||
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\svspace{-5mm} | ||
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\begin{center} | ||
\includegraphics[trim=260 0 140 0,clip, width=2cm]{img/mi2} | ||
\notesonly{\captionof{figure}{grouping of data} | ||
} | ||
\end{center} | ||
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\svspace{-15mm} | ||
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and/or\\ | ||
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\vspace{7mm} | ||
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\item possible directions in the data, by finding another continuous space for describing this data. \\ | ||
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Example: dimensionality reduction or an embedding space, $\vec u \in \R^M$ with $M < N$. | ||
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\svspace{-5mm} | ||
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\begin{center} | ||
\includegraphics[trim=0 0 400 0,clip, width=2cm]{img/mi2} | ||
\notesonly{\captionof{figure}{dimensionality reduction} | ||
} | ||
\end{center} | ||
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\end{itemize} | ||
\end{itemize} | ||
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\end{frame} |
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