The course covers a comprehensive range of topics aimed at securing operating systems against various threats. It begins with an exploration of different hardening approaches, identification of default configuration weaknesses, and the implementation of the Zero-Trust model for network security. Participants learn to manage trusted sources for Linux installations and third-party software, as well as the significance of drivers and libraries signing. The course addresses OS patching and updating processes for Windows and Linux, cryptography for encrypting storage in both environments, and certificates management for secure communication. Participants also gain knowledge and skills in access and authentication methods, including the Least Privilege Principle, Role-Based Access Control (RBAC), and privilege access management tools.
This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.
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.
This course makes you acquainted with the concept of systems-of-systems (SoS), which means that independent systems are collaborating. It gives you an understanding why SoS is an important topic in the current digitalisation and provides a theoretical and practical foundation for understanding important characteristics of SoS. It also gives you a deeper knowledge in a number of key concerns that need to be considered when engineering SoS.
Admitted students to this course may join the course any time between August 28 and October 6, 2023. With the recommended study pace of 25%, the course would take approximate seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than January 14, 2024.
Örebro University is offering a course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems.
After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University.
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