DESIGN FOR EXTENDED REALITIES

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.

This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.

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.

The course aims to provide knowledge about precision technology for Livestock production (Precision Livestock Farming, PLF) including its principles and frameworks, design and evaluation with a focus on end-user perspective and commercialisation.

The Internet of Things (IoT) is a networking paradigm which enables different devices (from thermostats to autonomous vehicles) to collect valuable information and exchange it with other devices using different communications protocols over the Internet. This technology allows to analyse and correlate heterogeneous sources of information, extract valuable insights, and enable better decision processes. Although the IoT has the potential to revolutionise a variety of industries, such as healthcare, agriculture, transportation, and manufacturing, IoT devices also introduce new cybersecurity risks and challenges. In this course, the students will obtain an in-depth understanding of the Internet of Things (IoT) and the associated cybersecurity challenges. The course covers the fundamentals of IoT and its applications, the communication protocols used in IoT systems, the cybersecurity threats to IoT, and the countermeasures that can be deployed. The course is split in four main modules, described as follows: Understand and illustrate the basic concepts of the IoT paradigm and its applications Discern benefits and drawback of the most common IoT communication protocols Identify the cybersecurity threats associated with IoT systems Know and select the appropriate cybersecurity countermeasures Course Plan Module 1: Introduction to IoT Definition and characteristics of IoT IoT architecture and components Applications of IoT Module 2: Communication Protocols for IoT Overview of communication protocols used in IoT MQTT, CoAP, and HTTP protocols Advantages and disadvantages of each protocol Module 3: Security Threats to IoT Overview of cybersecurity threats associated with IoT Understanding the risks associated with IoT Malware, DDoS, and phishing attacks Specific vulnerabilities in IoT devices and networks Module 4: Securing IoT Devices and Networks Overview of security measures for IoT systems Network segmentation, access control, and encryption Best practices for securing IoT devices and networks Organisation and Examination Credits and time table: 3 ECTS distributed over 10 weeks Scehduled online seminars: December 4th 2023, January 12th 2024 and February 9th 2024 Examination, one of the following: Analysis and presentation of relevant manuscripts in the literature Bring your own problem (BYOP) and solution. For example, analyse the cybersecurity of the IoT network of your company and propose improvements The number of participants in the course is limited, so please hurry with your application!