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The course is aimed at students majoring in technical translation, researchers, academic staff, etc.

The study course offers undergraduate students the opportunity to develop their knowledge, competences, and skills in applying and customizing the available machine-learning tools for textual data processing to solve a range of practical industry-related and research tasks including but not limited to corpus and textual data analysis, data preprocessing and representation, sentiment analysis, and machine translation applications. Students shall develop a comprehensive understanding of the nature of the contemporary multi-modal digital text considering, inter alia ethical, security, and sustainability aspects of textual data collection, processing, and representation. They will gain experience in the practical application of data and text mining approaches, data structuring, and data visualization techniques, learn to validate, segment, and reuse the results of textual data analysis using corresponding machine-learning methods, and develop skills in using qualitative and quantitative data analysis techniques.

The study course ensures that students develop a comprehensive knowledge of machine translation (MT) systems and their operation algorithms, getting insights into the functionalities of neural MT tools and terminology management systems (TMS), addressing term retrieval issues, analysing machine translation quality and its determinants, performing source text pre- and post-editing, as well as developing critical and creative thinking skills for the application of machine translation solutions in cultural heritage preservation projects. Students will develop competences and skills in using translation and terminology management systems, elaborate their content creation and editing skills using relevant machine translation tools to streamline workflows in the creation of multi-lingual multimodal content.

The Maritime Cybersecurity Management course introduces learners to the fundamentals of protecting modern maritime digital ecosystems, including ships, ports, logistics systems, and shore-based infrastructure. Through the story of the vessel Baltic Pearl and Captain Oto, participants explore cybersecurity concepts across seven engaging lessons with video lectures and quizzes, and an optional educational game, Sea Safe, gaining practical knowledge to recognize risks and apply security practices throughout the maritime sector.

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This short course provides participants with a practical and applied overview of industrial robotics in manufacturing. Over three sessions, the course covers the fundamentals of robot-based workplace design, the implementation of robotic operations (e.g., pick-and-place and grinding), and the use of ABB RobotStudio for virtual simulation and programming. Participants will gain hands-on experience in configuring robot work cells, programming robot movements, and optimizing robotic processes in a simulated environment.

This three-part short course introduces participants to the concepts and practical tools of production digitalization. The course provides hands-on experience with digital simulation, production performance evaluation, flexible systems design, and immersive VR technologies using the Visual Components software. Each module focuses on a key aspect of digital manufacturing and offers structured exercises in a simulated environment.

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The course provides an in-depth understanding of Robotic Process Automation (RPA), focusing on the design and use of RPA systems for structured business process automation. The course covers modern programming languages and management algorithms used in the development of RPA solutions. It examines methods and algorithms necessary for data management and process optimization, using intelligent system management methods. Theoretical exploration of programming libraries and development tools in the context of RPA is offered. The interaction of external input/output devices using system management and process automation methods is also discussed. The course analyzes data flow control methods. Through practical tasks, students are introduced to data processing methods, graph search algorithms, classification, optimization, machine learning, and big data processing methods, which are crucial for effective RPA implementation.

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The goal of this course is to study network management and control methods using Unix/Linux operating system environment.

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