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Management of Artificial Intelligence Solutions

Full-time studies
Full-time studies
  • Department
    Faculty of Fundamental Sciences
  • Program code
    6211BX024
  • Field of study
    Computer Sciences
  • Qualification
    Master of Informatics Sciences
  • Duration
    2

Fun fact

Artificial intelligence (AI) technologies are rapidly transforming industries and everyday life, offering powerful tools for automating processes, improving efficiency, and supporting data-driven decision-making. However, there remains a global and national shortage of specialists who can not only understand the potential of AI but also manage its development, integration, and application effectively.

About

The Management of Artificial Intelligence Solutions programme bridges the gap between technical AI development and strategic implementation. It provides students with a comprehensive understanding of how AI systems function while emphasising the management, coordination, and ethical deployment of these technologies across diverse sectors. 

The curriculum covers the full life cycle of AI projects (from conception and design to deployment and evaluation) equipping students to plan and oversee complex AI initiatives. While foundational technical knowledge is provided to ensure understanding of system operation, the main focus lies in the leadership, organisational, and decision-making aspects of AI project management. 

This programme welcomes students from both technical and non-technical backgrounds who aim to become intermediaries between AI developers and the industries that apply these innovations.

The goal of this master’s programme is to prepare highly qualified information systems professionals with strong analytical and managerial skills who can successfully lead AI-driven initiatives in a dynamic technological environment. Graduates will possess a broad understanding of artificial intelligence, be familiar with the AI system life cycle, and be capable of adapting, evaluating, and improving AI-based solutions in line with ethical and regulatory standards. 

To achieve these objectives, students engage in industry-driven challenges and participate in applied research projects. The study structure follows an intensive modular approach (each cycle focusing on one or two subjects over 1–2 months) allowing for deeper concentration and applied learning. Courses are delivered by Lithuanian and international lecturers, including industry experts, to ensure diverse perspectives and a strong global context. 

Possible research areas include: 

  • Analysis, modelling, and implementation of artificial intelligence solutions 

  • Research on AI methods, models, and technologies 

  • Evaluation and quality assessment of artificial intelligence systems. 

  • What will I be able to do?

    Upon successful completion of the programme, graduates will be able to:
    • Understand the principles and improvement possibilities of various artificial intelligence solutions
    • Plan, organise, and manage AI development and deployment projects
    • Conduct research to assess and select the most suitable AI technologies and methods
    • Evaluate the quality and compliance of AI systems with ethical, legal, and user accessibility standards
    • Lead interdisciplinary teams developing or implementing AI-based solutions, ensuring effective project execution and communication.

  • What are my career opportunities?

    Graduates of the programme can pursue careers as:
    • Artificial intelligence project managers
    • Data and AI solutions analysts
    • Process automation specialists
    • Researchers or consultants in AI application and innovation centres
    • Leaders of digital transformation and AI integration projects in both the private and public sectors.

Study subjects

1 - 2 Semesters

1 Semester

obligatory
  • FMITM24103 9 credits

    Artificial Intelligence System Engineering

    Module aim

    This course aims to equip participants with comprehensive knowledge and practical skills in managing Artificial Intelligence (AI) projects.

    Module description

    The course covers the entire artificial intelligence (AI) project lifecycle, from defining objectives and scoping to deployment and integration, participants will delve into project management methodologies, ethics, and legal considerations in AI development. The course includes hands-on exercises on recognizing and mitigating bias, addressing privacy concerns, and implementing cybersecurity principles. Participants will gain insights into adversarial attacks and defenses, privacy-preserving AI techniques, and documentation strategies. The course concludes with a focus on project closure, exploring success measurement, quality assurance, and emerging trends in IT and AI project management.

  • FMITM24102 9 credits

    Neural Networks (with course project)

    Module aim

    This course aims to provide a comprehensive understanding and practical skills of Neural Networks, from their historical development to advanced architectures and applications.

    Module description

    Participants will explore perceptrons, multilayer perceptrons (MLPs), and backpropagation algorithms, gaining hands-on experience in building neural networks. The course covers deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with a focus on architectural analysis and adjustment to project needs. Optimization techniques, architectural improvements, and interpretability methods will be explored, and participants will learn about unsupervised learning, self-supervised learning, and transfer learning concepts. The course concludes with applications of neural networks and future trends in this dynamic field.

  • FMISM24101 6 credits

    Fundamentals of Artificial Intelligence

    Module aim

    The aim of this course is to provide a comprehensive understanding of Artificial Intelligence (AI) and its application possibilities as well as limitations.

    Module description

    The course coverers such topics as Artificial intelligence (AI) definition, historical development, and its prominent role in computer science research. Participants will delve into key concepts and terminology in AI, gaining practical skills in data handling, cleaning, and feature engineering. The course will explore various supervised and unsupervised learning methods, including linear regression, logistic regression, decision trees, ensemble methods, clustering techniques, and dimensionality reduction. Emphasis will be placed on the practical application of tools and libraries for both supervised and unsupervised learning, allowing participants to build, test, and analyze models. Additionally, the course will cover data visualization techniques and explore trends and innovations in AI research, equipping participants with a solid foundation in AI concepts and practical skills for real-world applications.

  • FMITM24100 3 credits

    Master's Research Work 1

    Module aim

    After the search, analysis and generalization of literature on the selected topic to prepare or update a draft of Master Graduation Thesis task.

    Module description

    During the course Master’s Research Work 1, a field of research is selected; skills of information technology application, assessment and analysis of literature and data, planning of work, thoroughness in preparation of graphical and text documentation are mastered. After the search of scientific-technical information on the selected topic and its analysis, an individual or a group problematic task of Master Graduation Thesis is prepared or updated.

  • FMISM17100 3 credits

    Fundamentals of Scientific Research and Innovations

    Module aim

    To provide knowledge about the application of research methods and innovative solutions in the field of computer sciences and informatics engineering, to develop practical skills to prepare scientific and technical reports, master’s theses

    Module description

    The subject covers the methodology of scientific and engineering research, provides a broad overview of the most important research methods used in the field of computer sciences and informatics engineering, and provides basic knowledge about possible innovations in this field. Library research and other methods of information collection and data analysis are analysing, students are been taught to give out one’s ideas logically and argumentatively, and to describe complex material clearly and consistently. The subject also teaches how to write scientific, technical reports and other technical documents, prepare applications for research projects, and presents the process of planning and execution of research carried out in the master’s.

2 Semester

obligatory
  • FMITM24224 9 credits

    Management of Artificial Intelligence Projects (with course project)

    Module aim

    The course aims to provide a comprehensive understanding of the multifaceted role of AI in modern business, covering applications, strategy formulation, technical implementation and ethical leadership.

    Module description

    Participants will learn entrepreneurial leadership skills, addressing challenges unique to startups, fostering innovation, and navigating limited resources. The course delves into the intricacies of launching and scaling ventures, emphasizing effective leadership in marketing, sales, and social responsibility. Additionally, it explores crucial aspects of AI model management, from version control and monitoring to ethical practices and scalability. Participants will gain insights into DevOps, AIOps, user training, and system resilience, ensuring a holistic approach to AI deployment and operations.

  • FMITM24223 6 credits

    System Integration

    Module aim

    The aim of this course is to equip participants with a comprehensive understanding of system integration possibilities and key aspects of it.

    Module description

    This course explores the definition and significance of system integration, addressing key challenges and trends in the field. Participants will delve into integration architecture and components, covering relational databases and their optimization, as well as NoSQL databases and data warehousing. Data integration concepts and strategies, including Extract, Transform, Load (ETL) processes, will be examined, along with middleware technologies, message-oriented middleware (MOM), and web services with a focus on RESTful API design and integration. The course also encompasses authentication and authorization requirements, Single Sign-On (SSO) solutions, and cloud computing integration, including serverless and microservices architectures. The IoT ecosystem and devices, mobile app development, cross-platform integration, and the evaluation of integration success will be explored. Additionally, participants will review existing integration tools and platforms, concluding with an exploration of current research areas and the future of system integration and architectures.

  • FMITM24202 6 credits

    Natural Language Processing

    Module aim

    This course aims to provide a comprehensive introduction to Natural Language Processing (NLP), covering fundamental concepts, challenges, technologies to use and applications.

    Module description

    Participants will learn about text-based data sources, retrieval solutions, and data preprocessing techniques, including tokenization and text representation methods. The course delves into sentiment analysis, text classification, part-of-speech tagging, and named entity recognition, with practical exercises in building domain-specific systems. Participants will explore sequence labeling, sequence-to-sequence models, and neural language models for text generation. The course concludes with an overview of question answering, dialog systems, and a review of existing solutions in NLP, along with discussions on new research results and future trends in the field.

  • FMITM24221 6 credits

    Business Analytics

    Module aim

    The course aims to explore the pivotal role of analytics in contemporary business, tracing its historical evolution and emphasizing the analytics process from data collection to reporting.

    Module description

    Course topics include data quality, governance, process automation using solutions like RPA, and statistical techniques such as regression analysis and hypothesis testing. The curriculum covers time series analysis, forecasting methods, and explores linear and nonlinear optimization, including integer programming. Decision support through data analytics is addressed, covering areas like customer segmentation, supply chain analytics, and spatial analytics. A/B testing, key metrics for AI projects, and a review of Business Intelligence tools are included, along with an examination of the latest advancements in business analytics.

  • FMITM24200 3 credits

    Master's Research Work 2

    Module aim

    Prepare an analytical overview of scientific-technical literature on the selected topic and/or experiment based analysis of existing soslutions, prepare a plan for the further research.

    Module description

    During the course Master’s Research Work 2, knowledge in a chosen field of research is deepened, analytical review of scientific and technical information on the chosen topic is prepared and/or existing solutions are evaluated in executed experiments, research plan is prepared.

3 Semester

obligatory
  • FMITM24301 9 credits

    Cloud Computing and Big Data (with course project)

    Module aim

    This course aims to provide a comprehensive understanding of computer network fundamentals, cloud computing for AI, and the crucial role of big data, equipping participants with the knowledge and skills necessary for designing and implementing robust AI solutions in diverse technological landscapes.

    Module description

    This comprehensive course delves into the fundamentals of computer networks, cloud computing for AI, and the pivotal role of big data in artificial intelligence (AI). Participants will explore network protocols, architecture, and management alongside cloud computing concepts and services, emphasizing major providers like AWS, Azure, and GCP. The curriculum extends to data management in the cloud, containerization, and orchestration for AI applications. AI model deployment, integration with network services, and strategies for multi-cloud and hybrid cloud deployment are covered. The course also introduces edge computing for AI applications. The big data segment covers data collection, storage, management, processing, and distributed computing. Machine learning and deep learning with big data, as well as real-time analytics and streaming analytics for big data, are explored. The course concludes with a review of existing big data applications, solutions, current research areas, and future trends.

  • FMITM24323 6 credits

    Internship

    Module aim

    The aim is to provide students with hands-on experience, fostering their skills in AI project management, development ethical considerations, and collaborative teamwork, preparing them for leadership roles in the dynamic field of artificial intelligence.

    Module description

    This internship offers students a comprehensive exploration of AI project management and development in real-world settings. Through a hands-on approach, participants will delve into the foundations of AI project management, mastering the project lifecycle, tools, and technologies essential for the field. The program places a strong emphasis on ethical considerations, ensuring a responsible approach to AI practices. Engaging in team-based projects, participants will gain collaborative skills and industry exposure, with opportunities to interact with professionals in the AI domain. The internship culminates in a comprehensive assessment, evaluating participants based on their practical contributions to live projects and their ability to document and communicate project progress effectively. This immersive experience aims to prepare students for leadership roles in the dynamic landscape of AI project management.

  • FMITM24302 6 credits

    User Experience in Artificial Intelligence Solutions

    Module aim

    This course aims to provide a comprehensive understanding on the pivotal role of user experience (UX) in AI solutions, covering the architecture for scalable and user-centered AI systems.

    Module description

    Participants will explore technologies and methods for AI service implementation, with a specific emphasis on web system front-end development. The integration of AI services and APIs into front-end applications will be addressed, incorporating essential considerations for security. The course delves into user-centered design principles, including user research, personas, journey mapping, and usability testing. Design thinking principles will be applied to AI projects, covering ideation, prototyping, and usability evaluation. Additionally, participants will explore the design of conversational interfaces, chatbots, voice user interfaces (VUI), and natural language processing. The course extends into multimodal UX design, augmented reality (AR), virtual reality (VR), and the delicate balance between personalization and user control. Emerging trends and innovations in the UX research area will be discussed, providing participants with a comprehensive skill set for creating compelling and user-friendly AI interfaces.

  • FMITM24300 3 credits

    Master's Research Work 3

    Module aim

    To prepare the necessary environment, data and research tools, needed for master’s thesis research or the system being developed, receive the first results of the research.

    Module description

    During the course Master’s Research Work 3, knowledge is further deepened in the chosen research direction, concentrating not only on the analytical overview of the chosen topic of scientific and technical information, but also on real experiments, the primary analysis of their results. The scope of the results should be such that the obtained results could be presented at a scientific conference.

Free choice

4 Semester

obligatory
  • FMITM24400 30 credits

    Master's Thesis

    Module aim

    Complete the research provided for in the master’s thesis task, prepare the master’s thesis report and prove that the competence and abilities acquired during studies and scientific research correspond to the results provided for in the study program and the appropriate level of their achievement.

    Module description

    In the master’s thesis, the intended research is completed, a report is prepared, which motivates the choice of the research direction, an analytical review of the literature, research goals and tasks, justification of the choice of research methodology and equipment, and the results of the conducted research are presented, evaluated and summarized. At this stage, the material for the report at the scientific conference or scientific paper is also prepared, which is presented as an appendix to the report. The presentation in the conference and/or preparation of scientific paper is not mandatory, but recommended, indicating higher students competencies.

Statistics

Metric Value
Enrolled students 30
Enrolled to FT 28
Min FT grade 8.66