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.
Would you like to know what Industry 4.0 is about? Then this course is for you!
In the course, we look at enabling technologies of Industry 4.0 from a human and industrial perspective. The course covers many topics and you will learn the basic terminology related to Industry 4.0 as well as insight and understanding of the Fourth Industrial Revolution and how it is set to affect industry and individuals.
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.
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 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.
This course looks at where important materials in products we use every day come from and how these materials can be used more efficiently, longer, and in closed loops. This is the aim of the Circular Economy, but it doesn’t happen on its own. It is the result of choices and strategies by suppliers, designers, businesses, policymakers and all of us as consumers.
In addition to providing many cases of managing materials for sustainability, the course also teaches skills and tools for analyzing circular business models and promotes development of your own ideas to become more involved in the transition to a Circular Economy. You will learn from expert researchers and practitioners from around Europe as they explain core elements and challenges in the transition to a circular economy over the course of 5 modules:
Module 1: Materials. This module explores where materials come from, and builds a rationale for why society needs more circularity.
Module 2: Circular Business Models. In this module circular business models are explored in-depth and a range of ways for business to create economic and social value are discussed.
Module 3: Circular Design, Innovation and Assessment. This module presents topics like functional materials and eco-design as well as methods to assess environmental impacts.
Module 4: Policies and Networks. This module explores the role of governments and networks and how policies and sharing best practices can enable the circular economy.
Module 5: Circular Societies. This module examines new norms, forms of engagement, social systems, and institutions, needed by the circular economy and how we, as individuals, can help society become more circular.
Målet med kursen är att ge lärare fortbildning inom ämnet djurvälfärd och hållbarhet. Kursens mål är också att ge lärare inspiration att designa sin egen undervisning, att ge lärare möjlighet att ta till sig ny forskning och att dela med sig av läraktiviteter som kan användas av fler.