This course introduces students to the fundamentals of experiment design and statistical inference models for data analysis. The courses provides a hands on experience in designing an experiment, collecting data and drawing conclusions. Statistics in a science of decision making and conclusion drawing in the presence of variability. In rare cases, to be able to make a decision about a new product, or draw conclusions from an observed phenomena, we can collect observations from the entire population and as a result make a definitive conclusion about its attributes, but most often that is not the case. As a we often opt to performing an experiment, or making passive observation with only a-sub sample of the population that will help us extrapolate and make decision about the rest of the unobserved samples. This course is given on-line with three mandatory Zoom-meetings.  This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) which includes 15 credits of computer programming. In addition, English B/English 6 is required.

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that studies how computers process human language. This course allows learners to obtain knowledge about NLP problems, applications, and recent methods for solving them. The aim of this course is to expertise in applying neural networks and deep learning methods to solve ongoing problems in NLP. This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) which includes 15 credits of computer programming. In addition, English B/English 6 is required. Application is open until 22 March. This course is given on-line with three mandatory Zoom-meetings.

With probabilistic methods, you can make mobile robots navigate safely in shared environments using low-cost onboard sensors without expensive external infrastructure. In this course, we will introduce you to the application of probabilistic methods for automatic map building and accurate self-positioning. This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) which includes 15 credits of computer programming. In addition, English B/English 6 is required. Application is open until 25 February.

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.  

The course aims to provide an understanding of the application of software development in an environment based on the philosophy of lean and agile working methods. It includes an understanding of industry-relevant technologies to promote the development of software products as well as an understanding that value is of the utmost importance within businesses and organizations. The focus of the course is thus on creating a better understanding of the relationship between values and the influence of principles on businesses and organizations to achieve the best application of agile working methods and Lean as a philosophy.    

The mobile and connected world of today generates a large amount of data that needs to be managed, analysed, and linked. This is often done on the cloud. The development, deployment, and management of this is called Cloud Computing. The purpose of this course is to offer a wide background about designing, developing, deploying, testing, and monitoring a cloud solution, specifically with a focus on big data problems.

This course is a guide to the cybersecurity issues arising throughout the entire development process. We consider the development from the security perspective from the beginning stage until the final release and beyond. No matter whether you are a developer, engineer, or a top-level manager, this course will benefit you. You will learn some useful hands-on approaches for trade-off analysis, requirements prioritization methods, risk assessment approaches, and other security aspects at all stages of development. M1 - Introducing Security Development Lifecycle M2 - Security Trade-off Analysis M3 - Security in Requirements M4 - Security in Design M5 - Security in Development M6 - Security in verification M7 - Security after releace

Robot system 1 deals with industrial robots and their use in industry. The course addresses the structure and properties of robots, as well as principles for use in industry based on different principles. Based on the requirements placed on a robot system, these can be configured based on different technical points of departure, how they are to be used and methods for creating efficiency. Common equipment is taken up as adaptations to different work processes and applications. Here grippers and sensors come in as well as process equipment of various kinds. The course covers factors for investment, as well as laboratory work on how security and programming can be handled. This course is for professionals who work with production systems, automation and robotics at various levels as responsible for individual production lines, departments, or role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but principles that will be addressed will be applicable to a number of industries, including consulting companies working towards the manufacturing industry. The course contains the following: Industrial robots as a concept; structure and properties; robots in relation to concepts such as Industry 4.0 and Smart Industry Principles of use; configuration of robot system Laboratory work with safety and programming of industrial robots Development trends, global perspective

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.

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.

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.

Producing good machine learning models using the algorithms discussed in Introduction to Machine Learning – Part I typically depends on proper usage and preprocessing of the data, as well as the ability to interpret and act on the results generated by the method. Thereto, you will learn in this course more about the practical side of applying the machine learning techniques to actual problems including feature extraction methods, to figure out which part of the data is relevant to your problem, the bias-variance dilemma, to figure out if you have enough data for your model, ensemble learning, for combining more than one machine learning technique, and other experience-based practical recommendations. Armed with this knowledge, you will have to solve a classification problem in competition with the rest of the class, getting a hands-on experience in using Machine Learning.

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.

During the course, you will explore a variety of optimization problems related to production systems. You will gain hands-on experience on modern methods for practically simulating and optimizing production systems. You will collaborate in groups towards applying the gained knowledge on your own production cases through state-of-the-art optimization software. At the end of the course, you will be equipped with the theoretical knowledge and practical experience to implement optimization techniques on real-world production systems.

The course covers digitalisation and automation technologies and their application in smart factories. Technologies covered include simulation and deployment, digital twin, connectivity as an enabler for e.g. predictive maintenance, manufacturing execution systems and robotics.

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.

You will acquire knowledge about planning and implementing industrialization activities for achieving a faster time-to-market and time-to-volume with higher quality. During the course you will work on one of your company’s industrialization challenges as a “project case” and analyse ways to tackle them in an efficient way.

With the ongoing digitalization of the industry towards "Industry 4.0" i.e. smart factories with more efficient production, shorter lead times, higher quality, etc. increase the need to measure different quantities and associated processing of this measurement data. This includes the collection of signals via different types of sensors and signal processing of these signals in order to e.g. provide "smart industrial cyber-physical systems" (SICPS) with information for the purpose of monitoring, maintaining, controlling activities, etc. in order to achieve smart factories. The course will focus on sensors, analog-to-digital converters, quality of measured data and signal processing where we, among other things, highlights the choice of sensors and data collection systems and introduces some robust signal processing methods in the light of the digitization of the industry. This course is for professionals who work professionally with programming and development - regardless of industry or sector. You may be in the industry, the IT sector or a larger company in a completely different industry, you may be working in a development department within a company or as an IT consultant or. The course contains the following: Sensors for measuring vibrations, force, elongation, speed and associated signal conditioning Measurement uncertainty Analog-to-digital converter and "Effective Number Of Bits (ENOB)" Folding when sampling signals and anti-folding filters Fourier transform - Discrete Fourier transform Stochastic processes and relevant statistical concepts Power density spectrum, Power spectrum and associated systematic and random errors

The starting point for the course is Lean Production, with an understanding of principles and concepts that can lead to higher efficiency. Then we introduce computer-based application for modelling and simulation of production systems. This provides the opportunity to simulate a production system, perform experiments on it according to different conditions, and optimize the system to increase efficiency. This course is for professionals who work with production systems at different levels with a responsibility for individual production lines, departments, or has a role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but principles that will be addressed will be applicable to a number of industries, including consulting companies working towards the manufacturing industry. The course contains the following: Production systems and concepts such as Industry 4.0 and Smart Industry Lean Production and workshop with the Lean game Programming exercises with event-based simulation of production systems Analysis and optimization of production systems The majority of the course will take place on Campus (Växjö), and at a distance according to the schedule below. Some of the exercises are preferably attend on Campus, but we plan for it to be possible to follow the full course at a distance due to the Covid-situation.

Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University. This is course requires completion of course Reinforcement Learning part 1.  Read more