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Applied Informatics
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Summary of the Profile
General Description: Objective(s) of a study programme: The aim of the study programme is to train highly qualified information technology (IT) specialists who are able to: · apply artificial intelligence, machine learning and statistical methods for the analysis of big data (text, signals, multimedia), design, develop and deploy artificial intelligence and data analytics solutions; · formalise and model digital transformation processes, design, deploy and manage IT systems across a wide range of activities; · independently carry out research in business and academic setting, and pursue doctoral studies in Lithuanian and foreign universities and research institutions. Learning outcomes: Students completing this program will be able to: • Explain the relationship of acquired knowledge of artificial intelligence and data analytics to the results of mathematical science and applied research. • Apply data analytics knowledge and methods to solve problems in a wide range of specialised (and new) areas. • Apply knowledge of advanced (new) IT methods to the design and implementation of business digital transformation systems, assessing their need, impact and relevance to consumers, businesses and organisations. • Carry out interdisciplinary research in data analysis and modelling and apply the research results in practice. • Carry out interdisciplinary research in the analysis and modelling of systems and digital transformation processes, and to apply the research results in practice. • Analyse and process language, multimedia, signal and business data. • Analyse, formalise and model systems and processes of different complexity. • Analyse behavioural data of the IT system user and develop innovative human-computer interactions. • Present problems and proposed solutions clearly and convincingly in their native and foreign language, using reasoning, justification, reasoning and appropriate presentation tools, media and techniques. • Interact and collaborate professionally in a team, lead interdisciplinary IT product development and digital transformation project teams. • Plan independent learning and improve personal effectiveness as a basis for lifelong learning and continuous professional development. • Critically analyse the context of interdisciplinary research projects and IT system and product development, adapting to rapidly changing cultural, economic and technological environments. Activities of teaching and learning: Lectures, seminars, laboratory work, independent study, team projects, reading assignments, problem-solving exercises, laboratory sessions, experimental research, mid-term exams, and final examinations. Methods of assessment of learning achievements: Knowledge and skills are assessed using a ten-point cumulative grading system. Learning outcomes are evaluated throughout the semester via mid-term examinations, laboratory work, individual and group projects, as well as via a final examination. The final grade is calculated as the sum of the weighted grades for each component. Framework: Study subjects (modules), practical training:
The program totals 120 credits, encompassing master thesis. Core subjects include Machine Learning, Operations Research and Management, Neural Networks, Natural Language Technologies, Affective Computing, IT Governance, Information Modelling and Retrieval, Digital Image Processing, Information Visualisation, Social Media Analytics, Research Project No.1, Research Project No.2, Research Project No.3. Specialisations: None Optional courses: • Deepening your knowledge in the field of study by choosing specialized subjects such as Blockchain Technologies and Cryptography, Intelligent Internet of Things Systems, Gaming Environments and Technologies, Digital Transformation and ICT Infrastructure, Information Technology Team Leadership, Digital Marketing Strategies; • choose the topics of research projects and Master Thesis. Distinctive features of a study programme: The uniqueness of the study programme is in the programme content, focused on artificial intelligence, data analytics and system modelling, and also in the opportunity for students to shape their own basket of competences through the choice of elective subjects and the choice of research project and Master Thesis topics, thus orienting their studies either more towards the direction of artificial intelligence applications or towards digital transformation technologies. The programme facilitates student involvement in research by linking the topics of their research projects and Master theses to the research projects carried out at the Faculty. Access to professional activity or further study: Access to professional activity: Graduates of the study programme work as software architects and designers, data scientists, data engineers, artificial intelligence specialists, artificial intelligence engineers, engineers in optimisation of various IT processes (MLOps, AIOps, DataOps, DevOps), project managers of digitisation and software development projects, managers of IT departments in various organisations and companies, researchers, researchers and lecturers in scientific and educational institutions. Access to further study: Graduates can continue their studies in the PhD programme in Informatics or Informatics engineering.
