INTRODUCTION TO SUSTAINABLE DEVELOPMENT

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.

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

Vill du utveckla ditt ledarskap och samtidigt bli mer effektiv i din kommunikation? KI erbjuder nu utbildningen Motiverande ledarskap på 7,5 högskolepoäng. Kursen har fokus på ledarskap som identifierar och förstärker de beteenden inom en organisation som bidrar till verksamhetens mål. Då kommunikation är en central del i allt ledarskap ger kursen även praktisk träning i hur man genom kommunikation kan väcka medarbetarnas motivation. Om kursen Chefer och ledare har ett särskilt ansvar för att leda och utveckla organisationer, team och individer. Motiverande ledarskap (ML) baseras på den beteendeanalytiska organisationsteorin Organizational Behavior Management (OBM) och samtalsmetoden Motiverande samtal (MI). OBM bygger på forskning om vad som påverkar människors beteenden. MI är en samtalsmetod som syftar till att skapa motivation för att underlätta förändringsprocesser. Tillsammans ger metoderna chefer och ledare verktyg för att kunna styra och motivera sina medarbetares beteenden med direkt påverkan på organisationens prestationer och resultat. Syfte Kursen ger grundläggande kunskap och träning i att tillämpa ett motiverande ledarskap, med fokus på att initiera, facilitera och utvärdera utveckling och förändring utifrån teorierna MI och OBM. Ur kursinnehållet Kursen ger dig kunskaper och träning i hur du: Identifierar och utvecklar förändringsbara nyckelbeteenden inom den egna organisationen Planerar och genomför en organisatorisk förändringsprocess utifrån OBM Informerar och kommunicerar på ett effektivt och anpassat sätt utifrån MI Ger och tar emot lärande feedback Genomför utvecklingssamtal och krävande samtal, enskilt och i grupp Reflekterar över etiska frågor kopplat till ledarskap och motivation/beteendeförändring  

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.

The course is broken down into: Basic Bayesian concepts Selecting priors, deriving some equations Bayesian inference, Parametric model estimation Sampling based methods Sequential inference (Kalman filters, particle filters) Approximate inference, variational inference Model selection (missing data) Bayesian deep neural networks

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.