SMART HEALTHCARE WITH APPLICATIONS

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 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.

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

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.