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- \begin{document}
- \title{Notes for ``An Introduction to Statistical Learning''}
- \maketitle
- \tableofcontents{}\pagebreak{}
- \section{macros (ignore this section in PDF)}
- \begin{itemize}
- \item bold math symbol:
- \global\long\def\mathbold#1{\bm{{#1}}}%
- \item big parentheses wrapping an expression:
- \global\long\def\paren#1{\left(#1\right)}%
- \item $\paren{\frac{\frac{1}{2}}{3}}$
- \end{itemize}
- \section{Definitions}
- \begin{description}
- \item [{Supervised\ Learning}] analyzing an input to output relationship
- to make predictions or estimations about future outputs
- \item [{Unsupervised\ Learning}] learn structures and relationships from
- data
- \item [{Regression}] prediction of continuous or quantitative values
- \item [{Classification}] prediction of categorical or qualitative values
- \item [{Clustering}] grouping input data points, without predicting an
- output value
- \item [{Principal\ component}] todo
- \item [{Dimension}] todo
- \item [{Observation}] (see data point)
- \item [{Input\ variable}] one attribute of a data set
- \item [{Data\ point}] tuple of all input variables
- \end{description}
- \section{Mathematical notation of the book}
- \begin{itemize}
- \item $n$: number of (distinct) data points
- \item $p$: number of input variables
- \item $x_{ij}$: row-major interpretation: select from the data set $\mathbold X$
- the $i$-th data point, $j$-th variable, indexing starting at $1$
- \item $x_{i}$: select from the data set $\mathbold X$ the $i$-th data
- point, which will be a vector: $x_{i}=\paren{\begin{array}{c}
- x_{i1}\\
- x_{i2}\\
- \vdots\\
- x_{ip}
- \end{array}}$
- \item $\mathbold{x_{j}}$: (note the bold in contrast to $x_{i}$!) column
- $j$ of data set $\mathbold X$
- \item $x^{T}$: transpose $x$
- \item $\mathbold X$: matrix of $n$ rows and $p$ columns:
- \begin{align*}
- \mathbold{X=\paren{\begin{array}{cccc}
- x_{11} & x_{12} & \dots & x_{1p}\\
- x_{21} & x_{22} & \dots & x_{2p}\\
- \vdots & \vdots & \ddots & \vdots\\
- x_{n1} & x_{n2} & \dots & x_{np}
- \end{array}}} & =(\mathbold{x_{1}},\mathbold{x_{2}},\dots,\mathbold{x_{p}})=\begin{pmatrix}x_{1}^{T}\\
- x_{2}^{T}\\
- \vdots\\
- x_{n}^{T}
- \end{pmatrix}
- \end{align*}
- \end{itemize}
- \section{Formulas and so on}
- Lorem ipsum dolor sit amet consectetur adipiscing elit, urna consequat
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- nulla. Phasellus ante pellentesque erat cum risus consequat imperdiet
- aliquam, integer placerat et turpis mi eros nec lobortis taciti, vehicula
- nisl litora tellus ligula porttitor metus. Vivamus integer non suscipit
- taciti mus etiam at primis tempor sagittis sit, euismod libero facilisi
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- lectus nullam dapibus netus feugiat curae curabitur est ad. Massa
- curae fringilla porttitor quam sollicitudin iaculis aptent leo ligula
- euismod dictumst, orci\footnote{This is actually non-sense.} penatibus
- mauris eros etiam praesent erat volutpat posuere hac. Metus fringilla
- nec ullamcorper odio aliquam lacinia conubia mauris tempor, etiam
- ultricies proin quisque lectus sociis id tristique, integer phasellus
- taciti pretium adipiscing tortor sagittis ligula. Mollis pretium lorem
- primis senectus habitasse lectus scelerisque donec, ultricies tortor
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- enim et magna sit inceptos sodales parturient pharetra mollis, aenean
- vel nostra tellus commodo pretium sapien sociosqu.
- \end{document}
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