APPLIED CRYPTOGRAPHY

This course provides an understanding of the fundamental problems in software testing, as well as solid foundation in the practical methods and tools for a systematic state-of-the-art approach to testing of software.

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 

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 has the objective to provide proficiency in cybersecurity analysis and design in industrial settings, with a special focus on smart factories and Industry 4.0. To that aim, you will learn about advanced cybersecurity concepts, methodologies and tools. You will also be able to apply your knowledge to case-studies of industrial relevance.

In this course, participants are introduced to key notions and concepts evolving in sustainability science that are relevant to all, independent to one's work or field of interest. After having completed the course, participants will have a better understanding of the vocabulary used today and should demonstrate the ability to reflect critically to integrate different perspectives of environmental, social, and economic sustainability to their specific area of interest or research. Throughout the course, links are made to the Agenda 2030 for Sustainable Development, as our current global road map towards sustainability, and how new approaches and solutions are emerging to describe, understand and address key sustainability challenges. Put simply, the overall aim is to give participants the knowledge and confidence needed to present and discuss ideas with others by applying methods, concepts and the vocabulary exemplified in the course with a more holistic view on the sustainability agenda across topics and disciplines. The course is designed as 5 modules: The first module presents essential concepts within sustainability science, and methods used to describe, frame, and communicate aspects of sustainability. We look at key questions such as what we mean with strong or weak sustainability, resilience, tipping points and the notion of planetary boundaries. We also look at some techniques used of envisioning alternative futures and transitions pathways. The second module is all about systems thinking and how systemic approaches are applied today to achieve long-term sustainability goals. Your will see what we mean with systems thinking and how systems thinking, and design is applied in practice to find new solutions. The third module touches upon drivers for a sustainable future, namely links to economy and business with an introduction to notions of a circular economy, and also policy and regulatory frameworks. We introduce the basics of transformative policy frames and how they are designed and applied through several real-case examples. The fourth module discusses the links between innovation and sustainability, highlighting approaches for technological, social, institutional, and financial innovations. Some examples (or cases) aim to show how different actors across society balance in practice the need for innovative approaches for social, environmental, and economic sustainability. The fifth and last module provides general insights on how we work with models to create various scenarios that help us identify solutions and pathways for a more sustainable world. Three main dimensions are addressed namely climate and climate change, nature and biodiversity, and the importance of data and geodata science to support spatial planning and sustainable land use.

WHat you will learn Increased knowledge on sustainable cities and communities. Deeper understanding of the relationship between urbanization, decarbonisation and sustainability. Improved critical thinking on the opportunities and challenges for sustainable cities and communities as engines for greening the economy. Expanded ability to use systems thinking to assess sustainable cities and communities.   About this SpecializationIn this specialization you will learn how to drive change in cities and communities towards sustainable, climate friendly, just, healthy and prosperous futures, and you will boost your career with new knowledge, understanding and skills for navigating urban transformations. This specialisation brings together a series of cutting-edge courses with world-leading teachers on cities, communities, sustainability, governance and innovation. This specialization is offered by the IIIEE at Lund University and the City Futures Academy – an online learning community on urban transformations. Our flagship course, Greening the Economy: Sustainable Cities, is ranked in the Best Online Courses of All Time by Class Central. The ranking by Class Central contains 250 courses from 100 universities based on 170,000 reviews. Our specialisation builds on the success of the Greening the Economy: Sustainable Cities course.  A key approach embedded in the courses in this specialisation is the role of experimentation in urban transformations. In particular, urban living labs are highlighted as a means for catalysing change in cities and communities towards sustainable, climate friendly, just, healthy and prosperous futures. The experimentation within urban living labs offers the potential for accelerating transformations and systematic learning across urban and national contexts. Applied Learning Project Learners are introduced to key facts and insights about sustainable cities and communities as engines for greening the economy, then tasked with developing this understanding through readings and practice exercises that highlight the role of urban living labs in creating sustainable cities and communities. Specifically, you will learn: how to work with greening the economy through cities and communities; how to design and implement urban living labs for accelerating change in cities and communities; how to build resilience and create a host of benefits from nature-based solutions in cities and communities; and how to influence consumption patterns in cities and communities through sharing practices . Further documentaries and quizzes will provide you with critical thinking and a broader and deeper perspective that are essential to understanding and creating sustainable cities and communities.