Artificial Intelligence

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.

Today's ever-growing AI technology offers many different business opportunities. However, starting an AI project and maximizing the balance between business impact and resources is still a challenging task, requiring a thorough understanding of what AI can and cannot do for your business. This free course will give you an introduction to: What you can use artificial intelligence for How you as a business leader should approach AI from a business strategy perspective What key strategic decisions you need to make upfront What skills you need to succeed How you should start and proceed with different steps of your project The course was developed in collaboration with EIT Digital.

Today's ever-growing AI technology offers many different business opportunities. However, starting an AI project and maximizing the balance between business impact and resources is still a challenging task, requiring a thorough understanding of what AI can and cannot do for your business. This free course will give you an introduction to: What you can use artificial intelligence for How you as a business leader should approach AI from a business strategy perspective What key strategic decisions you need to make upfront What skills you need to succeed How you should start and proceed with different steps of your project The course was developed in collaboration with EIT Digital.

Discover the basics of computer vision and its role in Industry 4.0.Humans are in the midst of what is referred to as the Fourth Industrial Revolution, or Industry 4.0: the advent of new technologies that will forever change the face of business, and chief among them is computer vision. This three-week course from the Luleå University of Technology will give you a solid introduction to computer vision and help you explore its effects on industry and business. Once you complete the course, you’ll understand the potential applications of computer vision and be empowered to shape its future. This course will guide you through this journey to have a better understanding of the techniques that stand behind this field, and how you can get the benefit of using CV in your current business. The course will cover he concepts of the following fields, image processing, machine learning, deep learning, and use cases of computer Vision in business. Anyone in industry and academia who wants to boost their digital skills and gain confidence in how computer vision practices have evolved and might add a noticeable positive impact to their business and careers. This may include:  Employees, Executives, Directors, Senior Managers, Founders, and Entrepreneurs  Undergraduates and post-graduates  Researchers, teachers from majors related to business This course will be given in English.

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.

Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.

This course will teach you how to build convolutional neural networks. You will learn to design intelligent systems using deep learning for classification, annotation, and object recognition.

The course consists of three parts that introduce and explore the design of extended realities along different axes: a framing perspective, illustrating what XR is, how it has evolved, and how designing XR differs from traditional digital design practices; a methodological perspective, detailing those XR-specific theory and methods that address XR design issues; and a practical perspective, exploring best practices and concrete design activities through direct application of these to a case. Each part consists of lectures, readings, supervision, and an assignment centered on the specific topics discussed in the part of the course.Assignments are carried out by students individually and will be peer-reviewed first and then discussed with the teachers and the class using a design critique approach.

The real world is distributed. Distributed artificial intelligence is about this spread of knowledge, skills and abilities, across different units and different places. The course introduces Multi-Agent Systems - the main concept and technology for distributed AI. We will discuss how to build systems that can function as part of a Multi-agent System; how to organize different intelligent devices, how to use negotiation or auctions to distribute tasks to autonomous agents and many more topics from Swarm Intelligence to teams of intelligent robots.

EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) Artificial Intelligence is increasingly playing an integral role in our daily activities. Moreover, AI based solutions are used more and more in areas such as criminal justice, healthcare, and education, and therefore, their impact is high. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias, and a demand for model transparency and interpretability. Why did the system make this prediction? Do I trust it? What would happen if I change some parameter? As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand AI models. In this course, we present an introduction to Explainable Artificial Intelligence (XAI). We describe the challenges associated with the use of black-box models and how we can overcome such challenges using interpretable and explainable methods. Moreover, we study other aspects related to interacting with AI-based systems, for example, trust, acceptance, evaluation, and Fairness Accountability and Transparency-issues. The course is given in English and is targeted for working professionals in the industry. This course gives an introduction to Explainable AI (XAI), providing an overview of relevant concepts such as interpretability, transparency and black-box machine learning methods. The course provides an overview of state-of-the-art methods for generating explanations, and touches upon issues related to decision-support, human interaction with AI/intelligent systems and their evaluation. In summary, the Explainable AI course covers the following topics: Definitions and concepts such as black-box models, transparency, interpretable machine learning and explanations. Decision-making and decision support, Human-Computer Interaction (HCI) and AI. Explainable AI. Methods for Explainable AI. Applications and examples. Trust and acceptance. Ethical, legal and social issues of explainable AI. Evaluation methods and metrics. After a successful course, you will be able to: Show familiarity with concepts within Explainable AI and interpretable machine learning. Demonstrate comprehension of current techniques for generating explanations from black-box machine learning methods. Demonstrate comprehension of current ethical, social and legal challenges related to Explainable AI. Demonstrate the ability to select and assess Explainable AI methods. Demonstrate the ability to review, present and critically assess state-of-the-art papers in relevant areas within Explainable AI.

KursinnehållKursen syftar till att ge en introduktion och överblick av artificiell intelligens. Fokus ligger på att förstå begreppet och några viktiga tekniker som hur sökning och maskininlärning fungerar samt konsekvenser av AI på samhället.  Börja läsa när du vill Du kan börja läsa kursen i stort sett när du vill då kursen är en online-kurs med flexibel antagning. Du gör ansökan till den termin du tänker börja läsa kursen. Vill du börja direkt så ansöker du till innevarande termin, eller så väljer du den termin du tänker börja. Termin väljer du här ovan, så kommer du till rätt ansökningstillfälle. KursformatKursen är en distanskurs som görs i egen takt och hanteras i sin helhet i en web-baserad kursmiljö. Kursen baseras på självstudier av kursmaterialet och examineras med självrättande tester och inlämningar. Du som har gjort Elements of AI kan anmäla dig till den här kursen för att få dina resultat validerade. Det gäller både den svenska och den engelska versionen av kursen. Du måste inte göra om kursen, däremot måste du ladda upp certifikatet från Elements of AI och göra ett valideringstest med frågor motsvarande de som finns i Elements of AI för att säkerställa att det verkligen är du som gått igenom kursen. För mer information se denna länk. Kursen handleds över internet. Information om behörighetObservera att du vid ansökan till kursen måste kunna styrka att du har grundläggande behörighet. Om dina gymnasiemeriter inte redan finns på dina sidor på antagning.se så behöver du ladda upp gymnasieexamen, eller motsvarande, på antagning.se i samband med din ansökan.

KursinnehållKursen syftar till att ge en introduktion till praktisk AI, med fokus på grundläggande maskininlärning. Syftet är att förstå hur man kan skapa AI-system med hjälp av maskininlärning, få inblick i teknikens möjligheter och begränsningar, samt få en överblick över vanliga metoder för maskininlärning. Börja läsa när du villDu kan börja läsa kursen i stort sett när du vill då kursen är en online-kurs med flexibel antagning. Du gör ansökan till den termin du tänker börja läsa kursen. Vill du börja direkt så ansöker du till innevarande termin. Termin väljer du här ovan, så kommer du till rätt ansökningstillfälle.  KursformatKursen är en distanskurs som görs i egen takt och hanteras i sin helhet i en web-baserad kursmiljö. Kursen baseras på självstudier av kursmaterialet och examineras med självrättande tester och inlämningar. Du som har gjort Elements of AI, Part 2: Building AI kan anmäla dig till den här kursen för att få dina resultat validerade. Detta innefattar att göra ett valideringstest med frågor motsvarande de som finns i Elements of AI, Part 2: Building AI för att säkerställa att det verkligen är du som gått igenom kursen. För mer information se denna länk. Kursen handleds över internet. Information om behörighetObservera att du vid ansökan till kursen måste kunna styrka att du har grundläggande behörighet. Om dina gymnasiemeriter inte redan finns på dina sidor på antagning.se så behöver du ladda upp gymnasieexamen, eller motsvarande, på antagning.se i samband med din ansökan.