DEEP LEARNING FOR INDUSTRIAL IMAGING

This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation. You will learn: Understand algorithmic test generation techniques and their use in developer testing and continuous integration. Understand how to automatically generate test cases with assertions. Have a working knowledge and experience in static and dynamic generation of tests. Have an overview knowledge in search-based testing and the use of machine learning for test generation.

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.

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.

Why markets for electricity? How do they function? This introductory course explains how incentives shape outcomes in the electricity market. It brings out the implications for businesses and society of electricity pricing in the shadow of the energy transition. The course aims to provide a comprehensive overview of the electricity market's role in ensuring an efficient electricity supply and addressing key public questions, such as What is the purpose of the electricity market? Why do electricity prices vary by location? How can electricity prices surge despite low production costs? Are there alternative ways to sell electricity? Why is international electricity trading important? The course emphasizes the role of economic incentives in shaping market behavior and addresses critical issues such as market power and its consequences. You will also explore the inefficiencies stemming from unpriced aspects of energy supply and the role of regulation in mitigating these inefficiencies. As the global push toward decarbonization accelerates, the course delves into the challenges posed by large-scale electrification, the implications of climate legislation for energy systems, and the impact of protectionist national policies. The course offers a comprehensive introduction to the electricity market, provides you with analytical tools for independent analysis and brings you to the forefront of current energy policy debate. The course will enable you to Describe the interaction between the electricity system and the electricity market. Explain how the electricity market can increase the efficiency of electricity supply, e.g. with respect to market integration. Show how market power reduces the efficiency of the electricity market. Categorize fundamental market imperfections and describe their solutions. Explain economic and political challenges associated with the green transition. Apply economic tools to analyze the electricity market and examine how changes to the electricity system and regulation affect market outcomes. Target group This course is designed for engineers and managers eager to enhance their understanding of electricity markets within the context of the industrial green energy transition. The purpose is to increase the understanding of the scope of the electricity market and its role in achieving efficient electricity supply. Digital seminars The course includes five scheduled digital seminars. The seminars will be recorded to provide flexibility in completing the course, although we highly recommend to participate in the seminars if possible. November 4, 9:15 - 12:00 November 11, 9:15 - 12:00 November 25, 9:15 - 12:00 December 2, 9:15 - 12:00 December 16, 9:15 - 12:00   Study effort: 80 hrs