Educational Software Based on Matlab GUIs for Neural Networks Courses

Educational Software Based on Matlab GUIs for Neural Networks Courses

Pablo Díaz-Moreno (University of Valencia, Spain), Juan José Carrasco (University of Valencia, Spain), Emilio Soria-Olivas (University of Valencia, Spain), José M. Martínez-Martínez (University of Valencia, Spain), Pablo Escandell-Montero (University of Valencia, Spain) and Juan Gómez-Sanchis (University of Valencia, Spain)
DOI: 10.4018/978-1-5225-0159-6.ch075
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Abstract

Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the architecture of the network. This chapter presents a teaching tool for the its use in courses about neural models that solves some of the above-mentioned limitations. This tool is based on Matlab® software.
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Background

In the literature it is possible to find all kinds of educational software developed for different engineering courses related to data analysis and machine learning. In (Carrasco Fernández, 2012), the authors present an educational software developed using the MATLAB GUIDE tool. This software allows engineering students gain knowledge about data sets via the exploratory data analysis (EDA). This application includes models like clustering algorithms and self-organizing maps. However, does not include multilayer perceptrons. In (Deperlioglu, 2011) an educational tool to work with different kinds of neural network models is presented. The developed tool includes MLP, LVQ and SOM models. The design of the models was done visually and interactively but, in contrast to the proposed application in this chapter, confusion matrix or sensitivity-specificity histograms are not shown in the results. Moreover, in (Marković, 2014) a software system developed to support the teaching of Intelligent Systems is presented. The tool includes decision trees (ID3), clustering (kmeans), Naive Bayes, and perceptron models. In works (Ugur, 2010; Hwang, 2003; García Roselló, 2003; Zatarain, 2011) similar applications are presented.

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