DEEP LEARNING FOR INDUSTRIAL IMAGING

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 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.

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 course is broken down into: Basic Bayesian concepts Selecting priors, deriving some equations Bayesian inference, Parametric model estimation Sampling based methods Sequential inference (Kalman filters, particle filters) Approximate inference, variational inference Model selection (missing data) Bayesian deep neural networks

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.

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 hrs