INTRODUCTION TO VISION ON SENSING AND FRAMEWORKS

Today, the explosion of data has created new opportunities to apply machine learning (ML). Handling of the large amounts of data created by the very rapid digitization would not be possible without Machine Learning (ML). The purpose of the course "Introduction to Machine Learning" is to give you the foundation for ML. You will get an introduction to the basic areas of ML: data, statistics and probability for ML.

The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se. About the course Smart Healthcare with Applications, 4 credits Who is this course for?The course suits you with any Bachelor’s degree (equivalent of 180 Swedish credit points / ECTS credits at an accredited university) who have an interest in applying Artificial Intelligence (specifically Machine Learning) to healthcare. Leadership/management experience in health-related organization/industry OR a Bachelor degree in computer science is advantageous. What will you learn from this course?Healthcare as a sector together with other health-related sources of data (municipalities, home sensors, etc.), is now in a place and can take advantage of what data science, Artificial Intelligence (AI), and machine learning (ML) have to offer. Information-driven care has the potential to build smart solutions based on the collected health data in order to achieve a holistic fact-based picture of healthcare, from an individual to system perspective. This course aims to provide a general introduction to information-driven care, challenges, applications, and opportunities. Students will get introduced to artificial intelligence and machine learning in specific, as well as some use cases of information-driven care, and gain practice on how a real-world evidence project within information-driven care is investigated. What is the format for this course?Instruction type: The lectures, announcements, and assignments of this course will be fully online via a learning management system and presented in English. Each lecture is delivered through a video conference tool with a set of presentation slides displayed online during each class session. Online practical labs (pre-written Python notebooks) are also provided in the lectures.

The aim of this course is that students will learn about the analysis, design, and programming of deep learning algorithms. The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se. About the course Applied Deep Learning with PyTorch, 5 credits Who is this course for?This course provides the theoretical and practical aspects of deep neural networks. It is intended for students with a background in computer science and engineering. What will you learn from this course?Students will learn about the analysis, design, and programming of deep learning algorithms. The course has two modules: theory and practice. The theoretical content covers basic principles of multi-layer perceptions, spatio-temporal feature extraction with convolutional neural networks (CNNs), and recurrent neural networks (RNNs), classification and regression of big data, and generating novel data samples using generative models. The practical sessions cover the basics of programming with PyTorch. For instance, image classification and semantic segmentation using CNNs, future image frame prediction with RNNs, and image generation with generative adversarial networks. What is the format for this course?Instruction type: Teaching is in English and fully online. It consists of lectures, computer exercises, and project work. In the computer exercises, the student solves small problems using deep learning models. After programming various exercises, the participants will develop an advanced deep learning project. Participants will be encouraged to bring their own data. High-end GPU machines can be provided for the exercises and project.

  About the courseRenewable hydrogen stands out as a highly promising solution to decarbonize heavy industries and transportation sector, helping to achieve the climate goals of Sweden- reaching net zero emissions by 2045. The terms renewable hydrogen, clean hydrogen or green hydrogen refers to hydrogen produced from renewable energy or raw material. The utilization of renewable hydrogen for industrial applications necessitates the development of the entire value chain, from generation and storage to transport and final applications. Unlocking the potential of hydrogen economy in Sweden involves not only technological advancements and infrastructure development but also a skilled workforce. This course offers an introduction of renewable hydrogen as a pivotal component for industrial applications, focusing on its generation, storage, transport, and utilization within industrial contexts. Participants will gain a comprehensive understanding of the technical, economic, and environmental aspects of renewable hydrogen technologies, such as electrolysis, fuel cell, and hydrogen storage and distribution solutions, preparing them with essential knowledge and foundational insights for advancing the decarbonization of industrial processes through the adoption of hydrogen-based energy solutions. Aim and Learning OutcomesThe goal of this course is to develop a basic understanding of renewable hydrogen as a pivotal component for industrial applications, focusing on its generation, storage, transport, and utilization within industrial contexts.The learning outcomes of the course are to be able to: Explain the fundamental knowledge and theories behind electrolysis and fuel cell technologies. Compare and describe the differences of existing renewable hydrogen generation technologies (PEM, AE, AEM, SOE, etc.), and existing fuel cell technologies (PEMFC, MSFC, SOFC, etc.. Describe the principles of hydrogen storage, including gas phase, liquid phase, and material-based storage and thermal management of storage systems. Identify the challenges of hydrogen transportation and be able to describe relevant solutions. Examples of professional roles that will benefit from this course are energy and chemical engineers, renewable and energy transition specialists, policy makers and energy analysts. This course will also support the decarbonization of hard-to-abate industries, such as metallurgical industry and oil refinery industry, by using renewable hydrogen. This course is given by Mälardalen university in cooperation with Luleå University of Technology. Scheduled online seminars April 22nd, 2025May 19th, 2025 Study effort: 80 hours  

The course High-performance Computer Vision in the Cloud provides participants with the necessary tools and skills to navigate large-scale computing infrastructures, emphasizing scalability and performance optimization. Large computing infrastructures can be the key to driving the industry’s green transition. The course recognizes the instrumental role of large computing infrastructures in facilitating a green industry transition, enabling industrial actors to reduce environmental impact and optimize resource utilization, aiming to minimize energy consumption. The course covers concepts such as enabling technologies (e.g., CUDA), distributed computing, multi-core architectures, hardware versus software acceleration, container solutions(e.g., Docker and Kubernetes), as well as metrics and tools for monitoring performance and memory management, providing participants with a comprehensive skill set to lead environmentally responsible solutions in the digital era.  Scheduled online seminars  January 27th, 14:00-15:30 February 7th, 14:00-15:30 February 17th, 14:00-15:30 February 28th, 14:00-16:00 Entry requirements At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School.    

The course Leadership for Societal Change 1: Building Change Mindsets and Reflective Competencies is a course for you who want to actively participate in the industrial and societal change towards sustainability. The course aims to develop your reflective competence, make you aware of the learning that exists in your experiences, and master your further personal development as a transformative agent and leader. The course is structured around six delimited assignments that are completed separately. The tasks are taken from an assignment bank where you choose which tasks you do and in what order. The course ends with a summarizing assignment where you capture what you have learned through the six assignments completed. All courses within the Leadership for Sustainable Change course package are designed to be flexible and assume that it may be difficult to leave work and come to campus at specific dates and times. As a student, you can therefore complete the course assignments in your own order and at your own pace using a digital platform. This course is given by Mälardalen university in cooperation with Luleå University of Technology. Study effort: 80 hours