Engineering of Artificial Intelligence
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DepartmentFaculty of Fundamental Sciences
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Program code6211BX023
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Field of studyComputer Sciences
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QualificationMaster of Informatics Sciences
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Duration2
Fun fact
Artificial Intelligence (AI) is one of the most dynamic and transformative fields in modern technology, reshaping industries, economies, and societies worldwide. As the demand for AI-driven innovation continues to grow, so does the need for specialists capable of developing intelligent systems that enhance efficiency, sustainability, and quality of life.
While the United States currently leads in AI development, Europe is strategically focused on achieving major advances in this area. Consequently, the demand for highly skilled AI professionals in Europe (including Lithuania) is projected to rise significantly in the coming years.
About
The Engineering of Artificial Intelligence master’s programme is designed to prepare a new generation of AI specialists equipped with deep theoretical knowledge and strong practical skills. Students will gain expertise in AI principles, methods, and technologies, and learn to apply them across diverse data types (numerical, textual, visual, and audio). The curriculum emphasises hands-on learning through modern AI applications, focusing on technologies for data processing and generation, distributed systems, big data, and the Internet of Things. Ethical, legal, and user-acceptance aspects of AI implementation are also integral parts of the study process.
To enhance applied competencies, students engage in real-world challenges proposed by industry partners and participate in scientific research projects. The programme structure is organised into intensive study cycles (typically lasting one to two months) allowing students to master each subject area in depth. Lectures and workshops are conducted by Lithuanian and international experts from academia and industry, ensuring a comprehensive and globally informed learning experience.
Possible research areas include:
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Development of new artificial intelligence solution
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Research and advancement of innovative AI methods, models, and technologies.
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Modernisation of AI solution infrastructures.
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What will I be able to do?
Upon successful completion of the programme, graduates will be able to:
• Understand the principles, development, and improvement of various artificial intelligence solutions
• Create innovative AI systems using the latest technologies and research findings
• Conduct research on AI applications to generate new knowledge or identify optimal solutions
• Deploy AI solutions in real-world contexts, selecting suitable infrastructures and ensuring compliance with ethical, legal, and accessibility standards
• Lead interdisciplinary teams developing AI systems, coordinating technical implementation and communication among team members. -
What are my career opportunities?
Graduates of the Engineering of Artificial Intelligence programme can pursue careers as:
• Artificial intelligence systems architects or engineers
• Data scientists or AI solution developers
• AI infrastructure engineers or AI deployment and expansion experts
• Researchers in companies, innovation centres, or academic institutions focusing on AI applications and development.
Study subjects
1 Semester
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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.
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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.
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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.
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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.
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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
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FMITM24214 9 credits
Reinforcement Learning (with course project)
Module aim
This course aims to equip participants with a comprehensive understanding of Reinforcement Learning (RL) and its implementation technologies, application areas.
Module description
The course starts with foundational concepts such as the Markov Decision Process (MDP), participants will progress through dynamic programming techniques like value iteration, Monte Carlo methods, and temporal difference learning. The course covers model-free control methods like Q-Learning and SARSA, delves into function approximation and Deep Q-Networks (DQN), and explores policy gradient methods including REINFORCE and Actor-Critic approaches. The course addresses the crucial exploration vs. exploitation dilemma and introduces deep reinforcement learning techniques, such as Deep Deterministic Policy Gradients (DDPG) and Asynchronous Advantage Actor-Critic (A3C). Participants will also explore transfer learning and multi-agent RL, concluding with a review of existing applications and solutions, as well as an exploration of current research areas and the future of RL.
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ELESM24213 6 credits
Speech Processing
Module aim
This course aims to provide a comprehensive understanding of the core challenges and technologies of speech technology in computer science.
Module description
This course offers a thorough exploration of speech technology’s core challenges and applications in computer science. Participants will dive into digital signal processing for speech, covering feature extraction, acoustic modeling, and Automatic Speech Recognition (ASR) with Hidden Markov Models. The curriculum extends to language modeling, speech synthesis, and Text-to-Speech (TTS) systems, examining both concatenative and parametric approaches. Computational strategies for speaker and emotion recognition are explored, including multimodal techniques. Additionally, the course delves into Spoken Language Understanding through Natural Language Processing, addressing intent recognition and dialogue management. Deep Learning’s role in speech processing, including Convolutional Neural Networks and Recurrent Neural Networks, is highlighted. The course concludes with a focus on multimodal computational speech processing, reviewing existing solutions, exploring technical advancements, and discussing emerging trends like end-to-end ASR and TTS systems. Students must attend at least 60% of the scheduled time practical and at least half of the lectures according to the semester schedule.
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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.
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ELESM24211 6 credits
Image Processing
Module aim
This course aims to provide a foundational understanding of Computer Vision and Image Processing, covering key principles, existing technologies, and challenges in the field.
Module description
Participants will learn about image formation, color models, and various image processing techniques, including filtering, enhancement, and noise reduction. The course includes practical exercises in edge detection, image segmentation, feature extraction, and matching. Participants will explore object detection, recognition, image registration, and geometric transformations, along with a focus on deep learning applications in image classification. The course also delves into object tracking, video analysis, and concludes with a review of existing datasets and solutions in computer vision, discussing current research areas and future trends. Students must attend at least 60% of the scheduled time practical and at least half of the lectures according to the semester schedule.
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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
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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.
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FMITM24313 6 credits
Federated Learning and IoT
Module aim
The aim of this course is to delve into the fundamentals of IoT devices and networks, covering topics such as device architecture, communication protocols, security, and privacy considerations, while exploring advanced concepts like federated learning and its application in edge devices, multimodal data fusion, and emerging trends in the intersection of federated learning and IoT technologies.
Module description
Participants will gain practical insights into IoT design and implementation, focusing on data collection from IoT devices. The course then delves into the emerging field of Federated Learning, covering its architecture, algorithms, and applications on edge devices. Secure aggregation, differential privacy, and challenges in implementing federated learning on IoT devices will be thoroughly examined. Additionally, the course explores advanced topics, including multimodal data fusion, federated learning in 5G and beyond, and current trends at the intersection of federated learning and IoT research. Through a blend of theoretical understanding and hands-on applications, participants will be equipped to address real-world challenges in smart IoT devices and contribute to cutting-edge developments in federated learning.
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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.
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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.
4 Semester
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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 | 24 |
| Enrolled to FT | 24 |
| Min FT grade | 8.87 |