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From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence

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The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers in the second half of the 20th century that revolutionized scientific data analysis twofold: Tedious pencil and paper work could be successively transferred to the emerging software applications so sweat and tears turned into automated routines. In accordance with automation the manageable data volumes could be dramatically increased due to the exponential growth of computational memory and speed. Moreover highly non-linear and complex data analysis problems came within reach that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques that entered the agenda and considerably widened the range of scientific data analysis applications. Last but not least they 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. Difficult mathematical and algorithmic details are consequently banned for the sake of simplicity but are accessible by the referred literature. 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.

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