Titlebook: Research-Based Learning: Case Studies from Maastricht University; Ellen Bastiaens,Jonathan van Tilburg,Jeroen van Me Book 2017 Springer In

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University College Maastricht: The PEERS Project in perspective for the academic or the researcher who works with students in such a project. Working with students in projects like these implies that supervisors need to be actively aware of educational needs and appropriate interventions that help students reach a higher level of scientific think

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The Maastricht Science Programme: From Problem-Based Learning to Research-Based Learning in the Sciect periods within the curriculum, since the increasing freedom allows them to shape their own piece of research. In this way the students embrace a research attitude that helps them in the development to curiosity-driven lifelong researchers. This chapter elucidates the unique practical integration

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Data Science and Knowledge Engineering: From Knowledge Engineering to Knowledge Creationn their horizons and consider the exchange programme as an option. The recently introduced KE@Work programme, which enables excellent students to work part-time at a company during their study, will not only influence the participation in MaRBLe but also the current research profile of the bachelor

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es. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications..978-3-319-84834-1978-3-319-52156-5Series ISSN 1860-949X Series E-ISSN 1860-9503

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Jonathan van Tilburg,Jeroen van Merri?nboer,Ellen Bastiaensng on the output language, we can divide all approaches to automatic knowledge acquisition into two categories: symbolic and non-symbolic. Non-symbolic systems do not represent knowledge explicitly. For example, in statistical models knowledge is represented as a set of examples together with some s

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Ellen Bastiaens,Jeroen van Merri?nboer,Jonathan van Tilburgng on the output language, we can divide all approaches to automatic knowledge acquisition into two categories: symbolic and non-symbolic. Non-symbolic systems do not represent knowledge explicitly. For example, in statistical models knowledge is represented as a set of examples together with some s

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ng on the output language, we can divide all approaches to automatic knowledge acquisition into two categories: symbolic and non-symbolic. Non-symbolic systems do not represent knowledge explicitly. For example, in statistical models knowledge is represented as a set of examples together with some s