The analysis of experimental data is at heart of science from
its beginnings.
But it was the advent of digital computers that allowed the
execution of highly non-linear and increasingly complex data
analysis procedures - methods that were completely unfeasible
before. Non-linear curve fitting, clustering and machine learning
belong to these modern techniques which are a further step towards
computational intelligence.
The goal of this book is to provide an interactive and
illustrative guide to these topics. It concentrates on the road
from two dimensional curve fitting to multidimensional clustering
and machine learning with neural networks or support vector
machines. Along the way topics like mathematical optimization or
evolutionary algorithms are touched. All concepts and ideas are
outlined in a clear cut manner with graphically depicted
plausibility arguments and a little elementary mathematics. The
major topics are extensively outlined with
exploratory examples and applications. The primary goal is to be as
illustrative as possible without hiding problems and pitfalls but
to address them. The character of an illustrative cookbook is
complemented with specific sections that address more fundamental
questions like the relation between machine learning and human
intelligence. These sections may be skipped without affecting
the main road but they will open up possibly interesting insights
beyond the mere data massage.
All topics are completely demonstrated with the aid of the
commercial computing platform Mathematica and the Computational
Intelligence Packages (CIP), a high-level function library
developed with Mathematica's programming language on top of
Mathematica's algorithms. CIP is open-source so the detailed code
of every method is freely accessible. All examples and applications
shown throughout the book may be used and customized by the reader
without any
restrictions.
The target readerships are students of (computer) science and
engineering as well as scientific practitioners in industry and
academia who deserve an illustrative introduction to these topics.
Readers with programming skills may easily port and customize the
provided code.
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