Mälardalen University

This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. Students will learn how AI technologies, such as machine learning, optimization, and data analytics, are transforming traditional operational strategies, enhancing decision-making processes, and driving efficiency in energy and production operations. The curriculum will cover foundational concepts of AI and decision support systems, along with practical applications such as predictive maintenance, demand forecasting, process optimization, and real-time decision support. Through hands-on projects, case studies, and industry-relevant examples, participants will gain insights into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals. By the end of this course, students will be equipped with the skills to develop and apply AI-driven decision support systems to solve complex challenges in energy and production environments. This course is ideal for professionals and students interested in leveraging AI for operational excellence in the energy and production industries.

This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods.

Hur kan grön omställning göras så att resultatet är socialt önskvärt, miljömässigt -, samhälleligt - och ekonomiskt hållbart, samt etiskt acceptabelt? Själva uttrycket grön omställning leder lätt till ett snävt fokus på teknik och miljö. Det glöms att hållbar omställning syftar till att säkra framtida generationers möjligheter att leva sina liv inte bara i en god miljö, utan också i rättvisa och inkluderande samhällen och under hållbara ekonomiska förutsättningar. I den här kursen utforskar vi begreppet ansvarsfullhet och experimenterar praktiskt utifrån tankesätt och metoder som kommer ifrån EUs Responsible Research and Innovation (RRI) och innovationsforskningens Responsible Innovation (RI). Innehåll Ansvarsfull innovation och grön omställning EUs ansvarsfull forskning och innovation (RRI),  ingående delar och verktygslåda Hållbar utveckling och grön omställning Ansvarsfullhet och aktörer, intressenter samt användare Missions, framtider och grön omställning Du kommer få kunskap om Efter kursen kan du kartlägga området där den gröna omställningen skall ske, identifiera de intressenter som bör involveras den ansvarsfulla grön omställningsprocessen. Du kommer också att kunna identifiera arbetssätt som behöver förändras och kunna delta i utvecklandet av nya arbetssätt. Vem vänder sig kursen till? Den här kursen angår oss alla, oavsett yrkesroll och även som privatpersoner. Du bör vara nyfiken på samhällsutveckling mot hållbara samhällen i alla tre dimensioner. Du behöver inte ha några speciella förkunskaper förutom att du behöver vara beredd på att läsa engelska texter. Några som denna kurs kan vara särskilt lämpad för är strateger vid kommuner och regioner, utvecklare av teknik, organisationer eller samhällen, samt personer med ett samhällsengagemang som vill vara med och påverka utvecklingen.  Kursupplägg Kursen lanseras i maj och är en öppen kurs som kan genomföras online när som helst och inte kräver några förkunskaper. Kursen bygger på förinspelade filmer och poddar som vägleder och inspirerar inom varje kursområde. Dessa är kopplade till uppgifter och övningar du genomför och dokumenterar för ditt eget lärande. Språk Svenska

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.

In this course, we will go through different commands and techniques to create advanced shapes. You will also learn how to analyze the appearance of surfaces and shapes, and you will learn how to use top-down modeling to create models. You will also learn how to create products from sheet metal and products made from standard profiles. The software used is SolidWorks. This course is given in Swedish.

The main goal of this course is to teach you basic knowledge and skills in argumentation.You will be engaged in co-constructing evidence-based justifications as well as in analyzing existing justifications in search of argumentation fallacies. Individual work as well as group-based work will allow you to practice. You will analyze climate-related articles (published in scientific literature but also in the news) and will extract the implicit underlying arguments and provide their analysis.Ultimately, this course will help you to develop basic argumentative skills needed to critically join the debate in society on climate goals. Who is the course for?CLIMATE GOALS, ARGUMENTATION, EVIDENCE is aimed at anyone who is interested in moving the first steps into the argumentation domain with the purpose of joining the debate on climate goals.An engineer (but also a politician) is expected to have founded arguments before taking any (climate-related) action. A citizen is expected to have founded arguments before engaging and sustaining any climate-related political agenda. How is the course structured?The course is a 4-week course. Each week mainly focuses on a single Intended Learning Outcome.

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, you will get all the tools 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.

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.

This course teaches you how to build convolutional neural networks (CNN). You will learn how to design intelligent systems using deep learning for classification, annotation, and object recognition. It includes three modules: Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture. Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

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. You will gain an insight into the possibilities digital twins offer for improving production systems and processes. You will gain an understanding of when the use of digital twins can be a beneficial solution in the development of production systems. This is a course with flexible start: 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. You can join the course until the end of October.

Today, many industries face an increased demand for designing dependeble systems witch encounters various challenges, including more complex electronics and software-intensive systems. In the course, we will discuss different types of faults and possible sources of faults (technology, human and environment). Different types of faults are handled with different fault tolerance mechanisms, which are discussed for systems, hardware and software components. The course provides a solid foundation for understanding how to design fail-safe systems. The goal is to provide you with a toolbox of concepts for fail-safe design for both hardware and software so you can understand the rationale for appropriate mitigation strategies. The course is suitable for both engineers and students.