Abstract
Syllabi are rich educational resources. However, finding Computer Science syllabi on a generic search engine does not work well. Towards our goal of building a syllabus collection we have trained various Decision Tree, Naive-Bayes, Support Vector Machine and Feed-Forward Neural Network classifiers to recognize Computer Science syllabi from other web pages. We have also trained our classifiers to distinguish between Artificial Intelligence and Software Engineering syllabi. Our best classifiers are 95% accurate at both the tasks. We present an analysis of the various feature selection methods and classifiers we used hoping to help others developing their own collections.
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Rathod, N., Cassel, L.N. (2012). Machine Learning in Building a Collection of Computer Science Course Syllabi. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds) Theory and Practice of Digital Libraries. TPDL 2012. Lecture Notes in Computer Science, vol 7489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33290-6_38
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DOI: https://doi.org/10.1007/978-3-642-33290-6_38
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