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Artificial Intelligence

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AI for Managers

The purpose of the course “Artificial Intelligence for Managers” is to give managers and decision makers a principle understanding of AI and to increase their understanding of opportunities, difficulties, benefits, and risks connected to AI. It is neither an “Introduction to AI” nor an “AI for dummies” course. Instead, it is set to demystify AI and to transform it into an actionable tool for manages and decision makers. Target groupThis course is for product managers, project managers, executives, and engineering managers in organizations that have already made, or are about to make, the transition to working with AI. ContentThe course is organized in three modules. The initial module will focus an introduction to AI, giving an understanding of what type of cases can be addressed with AI and what managers need to know about AI technology. Module two will cover tools and concrete on how to set up an AI strategy and roadmap, how to get started on AI projects, how to integrate AI and IT development, how to (self) evaluate AI in use, and, not to forget, the ethical and legal aspects of AI. The third module will give the participants the chance to use their new knowledge and tools and work with their own practical cases and how they could be addressed using AI. The goal of the course is to empower the participants to:  Describe the principal concept of AI, its strengths, and shortcomings Understand opportunities, myths, and pitfalls of AI Identify problem areas in industry, society, and in management where AI could be utilized Analyze how AI can be applied in a particular problem area Manage an AI strategy and get started: implement a strategy and a roadmap to apply AI in a particular problem area Understand how to integrate AI with IT development Assess the maturity of AI utilization in an organization Reflect on applications of AI from an ethical and legal perspective as well as the future challenges (technical, organizational, social, etc.) Practical informationAll materials will be accessible and include reading material, lecturer slides etc. The lectures can either be attended live via Zoom or later using the recordings at a time that is convenient for the participants. There will be 3 onsite workshops with a focus on interaction with the teacher and the co-participants of sharing real-life experiences and insights. The course will be delivered in a flexible manner to facilitate the combination of course work with your ongoing professional commitments. The total effort to pass this course is typically around 200 hours. Teaching language: English Entry requirementsThe basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level. The course is free 

Applied Deep Learning with PyTorch

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.

Applied Machine Learning

The course aims to provide knowledge and skills in modern techniques and tools for machine learning, to create an understanding of its uses, strengths, and weaknesses. All in order to then apply these skills in a real world application. After the course, you will be able to:- describe basic terms, methods and approaches for machine learning- compare and argue for and against different types of machine learning methods for different application areas- use basic methods and various tools for machine learning- apply these techniques in a real world application as well as reflect and evaluate the results Course Content- Basic terms, methods and approaches for machine learning- Overview of different types of machine learning algorithms- Uses, strengths and weaknesses of the various types of machine learning- Overview of common tools and their applications- Neural networks (NN), convolutional neural network (CCN), recurrent neural networks (RNN), long short term memory (LSTM)- Machine learning project The course consists of a few lectures, a web-based theory exam, a series of laboratory exercises and a project assignment. The lectures present the necessary theory, tools, algorithms and basic terms, etc. The web-based theory exam consists of a quiz intended to examine basic terms and understanding. The labs are intended to provide basic skills and tools for machine learning. Finally, in the project, the student will demonstrate their own work on machine learning to merge previous knowledge. Depending on the student's previous experience in machine learning and programming ability, the work effort is estimated to be 80 hours of work.

Critical Design and Practical Ethics for AI

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.

Deep Learning for Industrial Imaging

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.

Design for Extended Realities

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.

Distribuerad artificiell intelligens and Multiagent System

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.

Machine Learning part 1

Örebro University is offering an introduction course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems. After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University. Read more  

Machine Learning With Big Data

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.

Predictive Data Analytics

The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling

Reinforcement learning del 1, 3 hp

Reinforcement learning - (RL) is a method for learning to make an optimal decision through trial and error. The goal of RL is to achieve an optimal policy for each state in a system. The course covers the underlying formalism of RL called Markovska decision-making processes and basic RL algorithms. Examples are dynamic programming. We will show how to model a problem as a Markovska decision-making process and implement basic RL algorithms to solve them. In addition, we will explore different ways to compare and evaluate the performance of learning methods.

Smart Healthcare with Applications

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.