Mälardalen University

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. 

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.

The course provides a basic understanding of how the product interacts with industrial processes and how this can made more efficient. You will also learn how to use time-to-volume with high quality as a strategy to achieve high productivity and low cost. The course gives you the tools and methods for managing industrialization challenges. By using these, you can ensure fast and cost-effective industrialization. The course also provides an overview of how you can reduce the risk of failure with your industrialization.

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.

A smart factory is an industrial manufacturing facility that uses technology such as the Internet of Things (IoT), automation, and artificial intelligence (AI) to increase productivity and profitability. Smart factories use sensors and other technology to collect, share, and analyze data that helps improve production, increase safety, reduce energy consumption, and improve product quality. You will learn The various major technological areas of smart factories Fundamental principles of operation and control of smart factories Understand and describe how smart machines and products interact in smart factories

Numerical models are used in every engineering task, from conceptual design to optimization, control, and diagnostics. As the process becomes more complex, data driven models are a powerful tool that allows to quantify relationships between available data and observations, which forms the basis for machine learning. Image recognition, spam filtering, and predictive analytics are some examples of how we can use data driven models. This course provides a simple introduction to fundamental techniques for dimensionality reduction, classification, and regression, which can be applied to all types of engineering problems.

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 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 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.