AI ETHICS FOR ENGINEERS

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.

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.

The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling

Örebro University is offering an introduction course in artificial intelligence. The course will address the basic concepts within classical artificial intelligence (other than machine learning). Traditional artificial intelligence is characterized by the so-called declarative approach to problem solving. The course deals with a selection of different intelligent problem-solving methods, both in theory and practice. After completing the course, the student will be able to model and use appropriate generic solution algorithms to solve problems in an intelligent system. The course is part of the education initiative Smarter at Örebro University. Read more

By taking part in this course you will learn about the role of visualization in industrial applications with a specific focus on Industry 4.0. You will learn how to evaluate and develop production systems and processes that integrate visualization technologies, such as digital twins, virtual and augmented reality and are supported by artificial intelligence.

Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.