Mälardalen University

Do you want to learn the basics of Industry 4.0, at your own pace, whenever you want? Then the MOOC (Massive Open Online Course) Introduction to Industry 4.0 is for you. You will learn basic terminology and theory while gaining insight and understanding of the fourth industrial revolution and how it affects us. The MOOC: Introduction to Industry 4.0 is part of MDU's investment in smart production. The course is divided into ten modules, each of which describes different technologies in Industry 4.0. We estimate that it will take about 40 hours to complete the course and it is in English. The MOOC can also give you eligibility to apply for these 3 university courses at Mälardalen University: Internet of things for industrial applications, 5 credits Simulation of production system, 5 credits Big data for industrial applications, 5 credits

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.

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 course has the objective to provide proficiency in cybersecurity analysis and design in industrial settings, with a special focus on smart factories and Industry 4.0. To that aim, you will learn about advanced cybersecurity concepts, methodologies and tools. You will also be able to apply your knowledge to case-studies of industrial relevance.

Learn how to improve industrial processes with modelling methods! Modeling is used to create a virtual representation of a real product. With the help of the model, you can study how the product works, test different options and evaluate the product before it is produced in reality. In this course, you gain knowledge on how to design and implement simulation models in the work of analyzing and improving production systems. You will learn how to plan and perform improvement studies, as well as apply the modeling process within the manufacturing industry. The course is given with flexible start and study pace, but we recommend a study pace of 20%, which means that the course takes about 8 calendar weeks.

The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling

The aim of the course is to introduce the participants into methods and tools for verifying systems that need to react to external stimuli. The methods use system models with precise formal semantics and will span model-checking as well as deductive verification. A set of simple examples as well as real-world applications will be used throughout the course to illustrate the methods and their tool support. The objective of the course is to understand the underpinning theories of formal verification, and learn how to apply tool support in order to verify system models.

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 purpose is to give the students an overview of issues and methods for development and assurance of safety-critical software, including details of selected technologies, methods and tools. The course includes four modules: Introduction to functional safety; knowledge that give increased understanding of the relationship between Embedded systems / safety-critical system / accidents / complexity / development models (development lifecycle models) / certification / “the safety case”. Analysis and modelling methods; review of analysis and modelling techniques for the development of safety-critical systems. Verification and validation of safety critical software, methods and activities to perform verification and validation. Architectures for safety critical systems. Safety as a design constraint.  

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

This course makes you acquainted with the concept of systems-of-systems (SoS), which means that independent systems are collaborating. It gives you an understanding why SoS is an important topic in the current digitalisation and provides a theoretical and practical foundation for understanding important characteristics of SoS. It also gives you a deeper knowledge in a number of key concerns that need to be considered when engineering SoS. Admitted students may join the course any time between September 2 and October 6, 2024. With the recommended study pace of 25%, the course would take approximate seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than January 19, 2025.

AI systems are increasingly being integrated into various industrial processes, including manufacturing, logistics, and autonomous vehicles. Trustworthy AI ensures that these systems operate reliably, reducing the risk of accidents or costly errors.  Trustworthy AI helps companies comply with ethical standards and legal regulations. It ensures that AI systems do not discriminate against certain groups, violate privacy rights, or engage in other unethical behaviors. Trustworthy AI System course can support in the development of more advanced AI technologies, fostering research collaboration, and attracting talent.