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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”

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

Lietišķās komunikācijas ar atsevišķām personām, personu grupām un institūcijām pamatprincipi

Unlock the power of processes to drive business success! This course equips you with the skills to design, optimize, and manage business processes that align…

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.

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. 

In the study course, students are presented with the necessary knowledge for designing adaptive systems, including: the concept of adaptive systems, development process throughout the…

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.