The course addresses fundamental questions related to how to build trusted systems. The focus will be on specific characteristics and approaches that allow to build trust into systems. In addition, methods to ensure that computers and services behave faithfully to the implementation specifications will be presented as well as approaches for detecting malicious deviations from the specifications. This course also introduces Blockchain concepts, security perspective of blockchain, consensus in blockchain, the decentralized philosophy behind Blockchain, as well as the main discussions in Blockchain environment and its potential applications.
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.
Do you want to be efficient, effective and minimize waste by learning and implementing lean production tools? This course provides insight into the demands and challenges posed by competitive production in industrial production systems and develops your ability to participate in and to drive improvement work.
The course focuses on efficient lean production. Through theory and project work, you will learn useful techniques, methods and strategies. You will obtain the necessary knowledge and training to carry out value stream mapping and other forms of improvement work.
The course offers current and competitive knowledge through its close links with our successful research and partner companies. It provides basic knowledge and understanding of the modern view of lean production in industrial activity.
In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data.
This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.This course is for experienced developers working in the industry. The course includes the following: Supervised learning, different types of data and data processing, Algorithms for handling text documents, Algorithms for handling data with numerical and categorical attributes, Neural Networks and Deep Learning for image recognition
ROS (Robot Operating System) is a common set of tools used in academia to do research within autonomous systems. It shortly provides a middleware for handling communication, as well as interfacing sensors and actuators, visualization, simulation and datalogging and infrastructure where it is easy to share your own methods and algorithms. The latter has allowed a large set of different of state-of-the-art research approaches to be readily available for downloading. Due to its popularity it is also getting more widespread in the industrial community, especially in R&D. This course will give you hands-on experience how to utilize these tools and apply them to a problem of your choice.
The promise of intelligent robot systems is that they can accomplish more tasks, more efficiently than a single-purpose industrial robotic solution. Intelligent robots act competently because they can plan, sequence and enact the actions that are appropriate in the context in which they find themselves. In order to achieve this capability, intelligent robots use Artificial Intelligence (AI) Search Methods. These are general-purpose algorithms for solving combinatorial problems, in other words, they constitute a robot's "reasoning engine". This course introduces students to the most important types of AI search methods. These are then instantiated in three industrially-relevant application contexts, namely, resource scheduling, motion planning, and multi-robot coordination.
As AI systems become more common and expand their abilities, the decisions they made have a crucial impact on society as a whole. Whether they are designed to recommend content or product online, to assist judges or physicians in their decision-making, or to decide how to distribute mortgages or video surveillance cameras, these systems can have a crucial and lasting impact on all of us. For this reason, it is of paramount importance that those in charge of designing such systems work toward ethical and responsible systems.
This course covers the theoretical and practical aspects of normative ethics and how they apply to AI systems, discuss how AI systems can become biased, as well as how to prevent and correct possible bias.
Through concrete examples, case studies, and project, this course aims at raising awareness on the problem of ethical AI as well as giving the students practical experience on how to ensure ethical and responsible development of AI systems in their everyday work.
This course is given on-line with three mandatory Zoom-meetings.
This course is directed towards working professionals.