Helsinki University of Technology →
Department of Computer Science and
Engineering →

Laboratory of Computer and Information
Science →
Teaching →

T-61.3050 Machine Learning: Basic Principles →
2007 →
Software

Some example codes distributed from the course web site are written in R. R is a language and environment for statistical computing and graphics. R can be considered as an open source implementation of S. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. It is often a vehicle of choice for research in statistical methodology. For example, R is currently the de facto data analysis environment in bioinformatics via the Bioconductor Project.

One of the strengths of R is excellent documentation, especially the numerous books written about it. Also the online documentation is quite extensive, with references to implemented methods. A good reference for R is "Modern Applied Statistics with S" by Venables and Ripley, available from the TKK library. Someone also recommended "Mixed-Effects Models in S and S-Plus" by Pinheiro and Bates, which is supposedly available as an eBook via the TKK library. I can't however confirm this because I have never succeeded in obtaining any eBook from our library, maybe you will have a better luck (or ask librarian). See the R web site for other documentation and references.

However, if you are more comfortable with some other software capable
of doing the necessary computations
there is no particular
reason to use R or S; unless you want to try R (after all, it is free).
It is important however to have familiarity with *some*
tool that allows you to do the basic operations and plots with the data.

Other options include but are not limited to:

- Commercial Matlab and its open source variant Octave.
- Weka, licensed under the GNU General Public License. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.

The standard unix command line utilities,
such as
`wc`

,
`sort`

,
`tr`

,
`uniq`

and
`sed`

, as well as scripting languages
like
`awk`

,
`perl`

and
`python`

can make the life easy in various phases
of data analysis, especially in converting the data file
to a suitable format or in doing some preliminary analysis or
pruning of the data. For example,
counting the number of unique entries
in the third column of a comma separated
data file:

`awk -F ',' '{print $3}' < data.csv | sort -u | wc -l`

These tools come in the default installations with operating systems of the unix family like Linux and OS X. For Microsoft Windows they are available for example via Cygwin.

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