INTRODUCTION TO MACHINE LEARNING

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.

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 Critical design and practical ethics for AI, 3 credits Who is this course for? Artificial Intelligence (AI) is being increasingly implemented and used in society today. It has already proven to have an impact on the individual, organization and society, and this impact will most likely only increase. Therefore, it is important to understand the ethical issues that may arise from use of AI, as well as to adopt a critical stance to the technology’s impact. The course introduces critical and ethical issues surrounding data and society, to train the student to problematize and reason about artificial intelligence (AI). You are most likely a designer, innovator, or product manager that works with digital services and products. What will you learn from this course? The course deals with different perspectives on AI and its real and potential effect on organizations and society. The course is based on five different perspectives on AI: accountability, surveillance capitalism, power and bias, sustainability, and trust. The course material consists of recent and relevant literature on the impact of, and critical perspectives on AI. Active discussions founded in different ethical perspectives are also an important part of the course. What is the format of this course? This course is primarily self-paced, with a few synchronous meetings. Most activities are based on the student’s having consumed specified material beforehand, such as video lectures, podcasts, articles, and books. Active discussions, both in online forums and during synchronous meetings, are an important part of the course.

How can we live a good life on one planet with over seven billion people? This course will explore greening the economy on four levels – individual, business, city, and nation. We will look at the relationships between these levels and give many practical examples of the complexities and solutions across the levels. Scandinavia, a pioneering place advancing sustainability and combating climate change, is a unique starting point for learning about greening the economy. We will learn from many initiatives attempted in Scandinavia since the 1970s, which are all potentially helpful and useful for other countries and contexts. The International Institute for Industrial Environmental Economics (IIIEE) at Lund University is an international centre of excellence on strategies for sustainable solutions. The IIIEE is ideally suited to understand and explain the interdisciplinary issues in green economies utilising the diverse disciplinary backgrounds of its international staff. The IIIEE has been researching and teaching on sustainability and greener economies since the 1990s and it has extensive international networks connecting with a variety of organizations.

In the era of shift towards green transition, industries face unique challenges and generates numerous opportunities. This course, "Intelligent Asset Management and Industrial AI" is designed to equip professionals with the knowledge and tools necessary to support advanced technologies in achieving environmental sustainability. Industries play a major role in contributing to the global economy that is accompanied with a significant share towards environmental degradation. The growing climatic concerns and degradation of natural resources has urged the need to reduce carbon footprints, minimize waste, and optimize resource utilization such that a green transition is achieved. Intelligent Asset Management and Industrial AI are at the forefront of this transformation offering innovative solutions to enhance operational efficiency, reduce environmental impact and support the industry’s commitment to sustainability. Furthermore, the course can help a professional to optimize the usage of resources, look for energy efficient systems, consider environmental changes, develop sustainable solutions, and integrate advanced technologies towards green transition. This is a problem-based course specific to an industrial sector. The problems can be provided by the course supervisor, or the participants can bring their own problems from their work. Common problems include e.g. asset management by balancing cost against performance, identifying, detecting, predicting, and planning for unexpected outages, disruptions or failures, exploring challenges and opportunities with AI and digitisation, monitoring the condition of industrial assets, and achieving sustainability goals. Target groupThe target group includes individuals working in various industries such as railway, mining, transportation, construction, manufacturing, logistics, energy, and other organizations that are or planning to implement asset management systems. This course can be suitable for professionals ranging from asset managers, maintenance and reliability professionals, operation managers, engineers, project managers, and asset management consultants. Online seminarsDecember 10th at 14.00 to 15.00January 14th at 14.00 to 15.00January 31st at 14.00 to 15.00February 13th at 14.00 to 15.00February 28th at 14.00 to 15.00 Entry requirements Bachelor’s degree of at least 180 ECTS or equivalent, which includes courses of at least 60 ECTS in for example one of the following areas: Maintenance Engineering, Mechanical Engineering, Materials Science, Data Science, Computer Engineering, Civil Engineering, Electrical and Electronics Engineering or equivalent. Or professional experience requirements four to five years of experience in relevant industries.