This short course introduces core Artificial Intelligence (AI) methods applied in engineering and industrial decision-making. The course focuses on optimization techniques, artificial neural networks, and multicriteria decision-making methods that are commonly used in real-world industrial problems.

 

Pamatkurss topošajiem būvinženieriem, lai tālāk apgūtu RTU studiju kursu “Būvdarbu tehnoloģija un darba drošība”

Artificial Intelligence Driven Process Control Systems introduces students to the principles, methods, and practical challenges of controlling industrial and cyber-physical systems using both classical and artificial intelligence-based approaches. The course begins with the fundamentals of process control, feedback, sensors, system modelling, and PID regulation, and gradually extends these concepts toward adaptive control, fuzzy logic, machine learning, reinforcement learning, digital twins, and intelligent industrial automation.

The course emphasises the practical integration of AI into real control architectures rather than treating AI as a stand-alone technology. Students learn how data from sensors can be processed, interpreted, and used to support prediction, optimisation, fault detection, adaptive decision-making, and supervisory control. Special attention is given to implementation constraints, safety, explainability, lifecycle management, human oversight, and industrial deployment, preparing students to design and critically evaluate AI-driven control solutions for modern manufacturing, robotics, energy, and process-industry applications.

Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including search, knowledge representation and machine learning.

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Lietišķās komunikācijas ar atsevišķām personām, personu grupām un institūcijām pamatprincipi

Take your first step towards advanced digital skills! This course is part of the RTU study course "Business Analytics" and is designed for a self-paced learning experience to provide an insight into the topic and spark interest. The course is freely accessible; however, it does not offer a certificate upon completion. The full-scale study course provides significant added value—it offers intensive practical work with digital tools and high-performance computing technologies, ensuring the development of advanced digital skills corresponding to levels 7–8 of the European Digital Competence Framework (DigComp). If you wish to study in-depth and receive a certificate certifying the acquired DigComp competences, apply for the full study course as a guest learner through the RTU Lifelong Learning Department: 👉 https://www.rtu.lv/lv/studijas/uznemsana/kursi-klausitajiem 📧 talakizglitiba@rtu.lv 📞 +371 67089439

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Course provides knowledge and practical tools for career planning, researching job market opportunities, and developing essential job search skills. Topics include career planning and self-awareness, analyzing job market trends, internship and job application strategies, and tips for a successful interview. The course provides insight into professional skills and workplace attitudes, as well as maintaining long-term career well-being, resilience, and networking. Practical worksheets and exercises support hands-on learning and career path development.

Studiju kurss apskata pamatprincipus un tehnoloģijas dzeramā ūdens sagatavošanai no dabas (virszemes, jūra, pazemes) ūdenstilpnēm. Studiju kurss paredz teorijas apguvi un iepazīšanos ar attīrīšanas shēmu aprēķinu principiem. Tiek apskatītā ūdens ķīmija un bioloģija, ūdens kvalitātes prasības, virszemes un pazemes ūdens tīrīšana, nogulšņu apstrāde un ūdens kvalitāte sadales tīklā. Studiju kursa mērķis ir izveidot studentu zināšanu sistēmu par ūdens sagatavošanas procesiem, attīstot prasmes ūdens sagatavošanas stacijas shēmas izstrādei.

This course is devoted to a relatively new area - data spaces, which is natural development of data lakes and other data management approaches and technologies introducing governance as a key to the AI-ready organisations. The governance is looked from different prospective, including privacy, security, transparency, monetisation and others. To add more technical and practical dimension to the course different EU initiatives are discussed and introduced for better understanding of the data spaces concepts an their implementations. 

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The study course explores the possibilities offered by edutainment methods for various language technology-enabled applications in such fields as translation, localization, and creation of multi-lingual content, including educational game design and localization. The study course is envisioned for undergraduate students of study programs in humanities, interdisciplinary STEM+, and information technology.

Students will engage in a series of case studies, hands-on tasks, and lectures to explore the current offer of edutainment IT solutions, learn to select, use, and customize them for particular learning and industry needs, and solve the problems of limited definition advancing their digital competence and skills to Level 5–6 according to DIGICOMP 2.2 (digital game-based language learning, translation process coding, use and customization of immersive learning platforms, translation gaming).

The study course is primarily aimed at developing high proficiency level competences and skills of the students mastering study programmes in different fields of humanities, social sciences, communication and human behavior sciences, interdisciplinary STEM+, and information technology. The study course is intended to provide a comprehensive overview of the fundamental issues associated with the retrieval, collection, organization, and processing of semantic and pragmatic data.  The students will get acquainted with the state-of-the-art in the area of natural language processing (NLP) and natural language generation (NLG). Upon completion of the study course, you will be able to:

• actively participate in the work of various NLP technology development teams,
• conduct research in the field of digital semantics and pragmatics and solve a range of knowledge management tasks,
• co-create and/or create solutions to complex problems with limited definition that are related to modifying, refining, improving and integrating new content and information into the existing knowledge of digital semantics and pragmatics to create new and original ideas.

Studiju kursa ietvaros tiek aplūkotas ekonomisko procesu prognozēšanā plaši izmantotas modeļu pamatklases, ieskaitot dažādu iespēju apskatu no mašīnmācīšanās jomas ekonomisko procesu prognozēšanai. Katrai modeļu klasei tiek pievērsta uzmanība nepieciešamiem pieņēmumiem un to pārkāpumu vājināšanas metodēm. Papildus studiju kursā tiek aplūkoti modeļa dokumentācijas izstrādes posmi, kā arī modeļa validācijas posmi atbilstoši ES un ASV regulatoru vadlīnijām.
Studiju kurss ir pielāgots sociālo zinātņu studentiem, kā arī kombinēto studiju metodikai ietverot asinhronas un sinhronas studiju aktivitātes ar nepieciešamiem atbalsta materiāliem. Studiju kursa īstenošanai tiek izmantota lietotājam draudzīga zinātniskās programmēšanas valoda "R" RStudio vai JupyterHub vidē uz augstas veiktspējas skaitļošanas platformas (HPC) bāzes. Visi studiju kursā studējošie apgūst Eiropas iedzīvotāju digitālās kompetences ietvaram (DigComp) atbilstošās augstāko līmeņu digitālās prasmes.

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Learn how real clinical domains generate and use medical data (imaging, lab results, ECG/EEG signals, pathology, and genomics) and how to process it responsibly for analysis and research.