This course emphasizes that systems-based changes are needed to achieve a sustainable world. In the past, dominant theories of change have neglected these complex conditions. In part, it includes the belief that change can be managed, planned, and controlled.
This course suggests more contemporary theories where you are more inclusive, being many stakeholders and use fluid ways of creating change. Similar compositions of ideas have been tested in the honours track Change Maker Future Track at LU School of Economics and Management.
At the end of the course, the participants will have a better chance of:
a. Understanding of the systemic nature of sustainability
b. Understanding of systems theory, and the concepts of complexity and wicked problems
c. Understanding of systems innovation and change
d. Having an overview of some tools for describing and analysing complex problems and contexts
e. Having an overview of contemporary theories of change
f. Having an in-depth understanding of the concept of Catalytic Leadership and Change
The course is now being updated and the new updated version will be launched at the end of February 2023.
This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation.
This course will teach you how to build convolutional neural networks. You will learn to design intelligent systems using deep learning for classification, annotation, and object recognition.
This course provides a fundamental knowledge of IoT, targeting physical devices, communication and computation infrastructure. The course gives theoretical knowledge as well as hands-on experiences to build an IoT application.
This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.