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.

Access to critical minerals and materials crucial to our wealth and well-being must be produced in a sustainable way. This means that the research must deal with metals and minerals that are innovation-critical, necessary for green/smart transition, rare, of insufficient supply or which should not be traded from conflict zones. Various component of the course makes it useful for professionals and hands-on with lectures, assignments, homeworks, fieldcourse and field reports as well as rock physics lab. Topics Sustainable exploration, mining and extraction of critical raw materials Course element: Critical and strategic raw materials Sustainability, SDGs, ESG and social aspects (the value chain) Exploration methods Geological and ore forming context Physical properties Geophysical methods Drilling technologies Extraction and mineral processing methods Rock quality and mining methods Nano-tech solutions Ground water contamination and accessibility Environmental assessments Mine tailing and beneficiation Site visits and hands-on (Epiroc, Blötberget, labs) Course structure The course is a combination of in-person, hybrid and hands-on including field trips. You will learn By the end of the course, you will be able to: analyse what exploration methods are used for what commodities, have good knowledge of the state-of-the-art solutions and incorporate your learning in todays industry practices. Who is the course for? This course is designed for those who are geologists, engineers or work with sustainability to learn how critical raw materials are explored, mined and turn to metals. It is open to both university students but also industry participants from relevant sectors. How much time do I need for the course? The course will run from 25 August - 5 December 2025 and will in sum require 100 hrs of commitments. Check the SERC center for more updates: www.smartexploration.se