Mälardalen University

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. You will learn: Understand algorithmic test generation techniques and their use in developer testing and continuous integration. Understand how to automatically generate test cases with assertions. Have a working knowledge and experience in static and dynamic generation of tests. Have an overview knowledge in search-based testing and the use of machine learning for test generation.

This course will provide a basic theoretical and practical knowledge in the art of configuring and securing computer networks and create simpler topologies. Together with the "Computer Networks II" distance course (Datakommunikation i nätverk II, distanskurs) you will be covering most, but not all, of the content that are part of a Cisco Certified Network Associate (CCNA) certificate. The certificate is not part of this course.

In this course, you will learn how data analysis in virtual production can improve your organization's results! Data analytics in virtual production uses advanced techniques to collect, analyze and present data to improve production. This system is designed to help companies optimize their production and increase efficiency. By learning how to model, do scenario analysis and evaluate using industrial software, identify bottlenecks, and use AI methods and applications, s necessary to succeed with a full production analysis. 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.

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.

Learn about digital twins and how they can be used in smart production! A digital twin is used to create a virtual model of a real production system. Among other things, it can be used to simulate how the product will be manufactured, how materials flow and how machines move. The course gives you knowledge of industrial digital twins and their application within the framework of smart production. 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.

In this course you will learn how to design production systems using XR. By visualizing production processes using various XR technologies, you will gain an understanding of when each technology is best suited and how it can be implemented.

Today, many industries face an increase in the design of dependable systems, often with a multitude of challenges including more complex electronics and intensive software. At the same time, most of the engineers graduating from universities do not have skills in designing fault tolerant systems.  This online course aims to give engineers and students a toolbox of fail-safe design concepts, addressing both hardware and software techniques, such that they can understand the rationales for suitable mitigation strategies.

In this course, you will be made aware of the state-of-the-art in cybersecurity research and state of practice in industry. Cybersecurity vulnerabilities are a threat to progress in the business sector and society. This is an accelerating threat due to the current rapid digitalisation, which in manufacturing is termed Industry 4.0. Companies are aware of this threat and realise the need to invest in countermeasures, but development is hampered by lack of competence.  

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

Do you want to be efficient, effective and minimize waste by learning and implementing lean production tools? This course provides insight into the demands and challenges posed by competitive production in industrial production systems and develops your ability to participate in and to drive improvement work. The course focuses on efficient lean production. Through theory and project work, you will learn useful techniques, methods and strategies. You will obtain the necessary knowledge and training to carry out value stream mapping and other forms of improvement work. The course offers current and competitive knowledge through its close links with our successful research and partner companies. It provides basic knowledge and understanding of the modern view of lean production in industrial activity. 

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.

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.

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.

The aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.

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.  

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.