HANDLING UNCERTAINTY IN ARTIFICIAL INTELLIGENCE A1N

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.

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 aims to give insights in fundamental concepts of machine learning for predictive analytics to provide actionable, i.e., better and more informed decisions in, forecasting. It covers the key concepts to extract useful information and knowledge from data sets to construct predictive modeling. The course includes three modules: Introduction: overview of Predictive data analytics and Machine learning for predictive analytics. Data exploration and visualization: presents case studies from industrial application domains and discusses key technical issues related to how we can gain insights enabling to see trends and patterns in industrial data. Predictive modeling: consists of issues in construction of predictive modeling, i.e., model data and determine Machine learning algorithms for predicative analytics and techniques for model evaluation.

Opens in May 2025. The Swedish version of the course, namely ”Varför välja trä vid nästa byggprojekt?” is already open. For more iformation contact course coordinator dimitris.athanassiadis@slu.seCourse DescriptionDifferent types of biomaterials (e.g., wood) are crucial in the challenge of decarbonizing the built environment and reducing the carbon footprint of buildings and infrastructure by replacing materials like steel and cement, which have high carbon dioxide emissions. At the same time, we must not forget that it is important to preserve biodiversity and the social values of our forests. The 13 modules of the course cover many forestry related subjects, including harvesting methods, biodiversity, forest management, logistics, the role of forests in the climate transition, carbon storage, environmental benefits of multi-story buildings with wood, and more. The goal is that participants will gain a shared understanding of Swedish forestry so that they can make well-informed decisions about material choices for their next construction project. Course PeriodThe course will be active for 3 years. Content Forest history: The utilization of forests in Sweden throughout the past years Forestry methods and forest management Forest regeneration Wood properties Forest mensuration Forest tree breeding The forest's carbon balance Business models and market development: Focus on wood high rises Nature conservation and biodiversity in the forest Course StructureThe course is fully digital with pre-recorded lectures. You can participate in the course at your own pace. Modules conclude with quizzes where you can test how much you have learned. You will learn aboutUpon completion of the course, you will have learned more about various forest-related concepts, acquired knowledge of forest utilization in Sweden throughout the past years, increased your understanding of forest management and how different management methods affect biodiversity in the forest, and learned about the forestry cycle—from regeneration to final harvesting, etc. Who is this course for?This course is designed for professionals such as architects, municipal employees working with urban planning and construction, individuals in the construction and civil engineering sector, and those in other related fields. This is an introductory course and will contribute to upskilling of the entire construction sector, thereby increasing the industry's international competitiveness while also providing important prerequisites for the development of future sustainable, beautiful, and inclusive cities. Since the course is open to everyone, we hope that more groups, such as students, doctoral candidates, forest owners, and others with an interest in forestry, will take the course and engage with inspiring lectures where scientific knowledge primarily produced within SLU (Swedish University of Agricultural Sciences) is presented.

Målet med kursen är att ge lärare fortbildning inom ämnet djurvälfärd och hållbarhet. Kursens mål är också att ge lärare inspiration att designa sin egen undervisning, att ge lärare möjlighet att ta till sig ny forskning och att dela med sig av läraktiviteter som kan användas av fler.

This course is offered on-demand, meaning that it will begin as soon as at least 10 participants have registered. Once the threshold is reached, the course will start shortly thereafter. Batteries and battery technology are vital for achieving sustainable transportation and climate-neutral goals. As concerns over retired batteries are growing and companies in the battery or electric vehicle ecosystem need appropriate business strategies and framework to work with.This course aims to help participants with a deep understanding of battery circularity within the context of circular business models. You will gain the knowledge and skills necessary to design and implement circular business models and strategies in the battery and electric vehicle industry, considering both individual company specific and ecosystem-wide perspectives. You will also gain the ability to navigate the complexities of transitioning towards circularity and green transition in the industry.The course includes a project work to develop a digitally enabled circular business model based on real-world problems. Course content Battery second life and circularity Barriers and enablers of battery circularity Circular business models Ecosystem management Pathways for circular transformation Design principles for battery circularity Role of advanced digital technologies Learning outcomes After completing the course, you will be able to: Describe the concept of battery circularity and its importance in achieving sustainability goals. Examine and explain the characteristics and differences of different types of circular business models and required collaboration forms in the battery- and electric vehicle- industry. Analyze key factors that are influencing design and implement circular business models based on specific individual company and its ecosystem contexts. Analyze key stakeholders and develop ecosystem management strategies for designing and implementing circular business models. Explain the role of digitalization, design, and policies to design and implement circular business models. Plan and design a digitally enabled circular business model that is suitable for a given battery circularity problem. Examples of professional roles that will benefit from this course are sustainability managers, battery technology engineers, business development managers, circular developers, product developers, environmental engineers, material engineers, supply chain engineers or managers, battery specialists, circular economy specialists, etc. This course is given by Mälardalen university in cooperation with Luleå University of Technology. Study effort: 80 hours