COURSE DESCRIPTION
This course explores how we can design, create and achieve climate neutral cities. We embrace the “mission to the moon” approach for tackling greenhouse emissions from cities putting an emphasis on pathways and opportunities. We utilise insights and inspiration from Sweden, Europe and around the world.
We target how to support individuals and organisations in developing transformative skills and capacities for action on climate neutral cities. We focus on mitigation of greenhouse gas emissions but also connect to adaptation, resilience, social justice and sustainable development in the context of cities, climate and change.
The course is designed around 5 interconnected modules. We therefore created a format that provides a diversity of ways to learn and creatively engage with the content.
Module 1: Visions and Plans. In this week we begin with looking at visions for climate action and the plans or strategies on how to achieve ambitious goals.
Module 2: Data and Tools. In this week we explore tools for climate action and creating both immediate and long-lasting impacts.
Module 3: Finance and Partnerships. In this week we tackle the key challenge of financing climate action and the vital role of partnerships.
Module 4: Engagement and Action. In this week we delve into community and citizen engagement and how it underpins climate action.
Module 5: Research and Innovation. In this week we connect climate action to research, evaluation and innovation.
This course will be launched on Oct 25, 2023.
Today, the explosion of data has created new opportunities to apply machine learning (ML). Handling of the large amounts of data created by the very rapid digitization would not be possible without Machine Learning (ML). The purpose of the course "Introduction to Machine Learning" is to give you the foundation for ML. You will get an introduction to the basic areas of ML: data, statistics and probability for ML.
The aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.
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.
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.
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