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

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