Zoekresultaten

Testing machine learning applications

In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.

Understanding and governing learning in sustainability transitions

Many transitions scholars underscore the importance of learning in sustainability transitions, but the associated learning processes have hardly been conceptualised. The diverse, well-established research fields related to learning are broadly ignored or loosely applied. In this paper, we systematically explore four interesting learning traditions in terms of their value for gaining an in-depth understanding of learning in sustainability transitions and their relevance for fostering learning, by connecting them to key features of transitions. The selected learning traditions from different disciplinary backgrounds provide valuable insights. None of them sufficiently addresses the complexity of transitions. They include, however, a diversity of relevant learning contexts. We conclude that they have value for investigating new areas such as learning in socio-technological regimes and in later phases of a transition, while enlightening forms of learning that have not yet been fully recognised in transition studies, such as superficial learning, unlearning, and learning to resist change.

The (im)possibility of new learning and selfdirection.

In this chapter it is argued that self-direction is currently well above the head of the majority of youngsters and even of many adults. Evidence for this conclusion stems from developmental and brain research. However, for various reasons it is important that people develop the competences that are necessary for self-direction. To what degree is it possible to develop these competences? Are they 'learnable'? What can education contribute?

Social learning as an analytical lens for co-creative planning

This article highlights the psychological dimension of social learning. Insights from psychology address the interrelated role of personal and group dynamics in social learning. This can provide a useful starting point for a rewarding use of social learning as an analytical tool in co-creative planning. Such an approach to social learning proves beneficial to (i) identify both positive and negative potential effects of social learning, (ii) untangle hidden power relationships at play at individual and small group levels in relation to social psychological factors, and (iii) discern the role of individuals and small groups within their larger contexts. The findings are empirically illustrated with a case of incremental urban development in Groningen, the Netherlands.

Lab Amsterdam

Amsterdam as a lab. That is what Amsterdam University of Applied Sciences' three fieldlabs and its many partners have in mind. Functional illiteracy, debts, learning deficiencies or problems caused by extreme precipitation: the city contains plenty of tough issues, demanding novel approaches in which co-creation and participation by residents, social organizations and knowledge institutions are basic principles.In the fieldlabs, people try to change current practices by working with, instead of for or on behalf of those whom it concerns. But how to achieve effective learning environments between parties? How to encourage stakeholder participation in complex issues? And how to build new relations and roles?

Ensembles of incremental learners to detect anomalies in ad hoc sensor networks