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Artificial intelligence
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Summary of the Profile
Objective(s) of a study programme:
To provide competences necessary to develop artificial intelligence (AI) based computational systems, which replicate human-made decisions with a particular emphasis on algorithms for data processing and analysis, machine learning, image and speech recognition, social and economic aspects of AI application.
Description of the study programme: https://admissions.ktu.edu/programme/b-artificial-intelligence/
Learning outcomes:
Knowledge and its application:
Is able to explain fundamentals in mathematics, physics, cognitive neuroscience and understand of their relations to research and application of artificial intelligence.
Is able to explain in detail fundamentals in design and analysis of algorithms, programming languages and technologies, software life cycle and engineering processes, to apply them in software development.
Is able to consistently explain basic operational processes and limitations of up-to-date hardware and software, main architectures of computer networks, fundamentals of secure network communication.
Is able to consistently explain concepts, methods and practical applications of system modelling, data collection and analysis.
Is able to consistently explain artificial intelligence processes including expert systems, rule-based and logic-based systems and machine learning.
Is able to consistently explain algorithms of speech and image recognition, identification and segmentation, to determine their fields of applications.
Is able to describe overall digitization processes, evolution of informatics and artificial intelligence, foresee possible tendencies in future applications of artificial intelligence.
Is able to consistently explain ethics guidelines in artificial intelligence, legal requirements including data protection, rights of intellectual property, product safety problems and rules of professional ethics, and understanding how to design trustworthy human-oriented artificial intelligence systems.
Is able to evaluate influence of the changing context in business, technology, social and legal sectors on the artificial intelligence technologies and vice versa.
Research skills:
Is able to perform analysis for input and output data of the system.
Is able to design imitational model of a system, select fundamental processes, validate and verify the model.
Is able to evaluate efficiency of an algorithm in the intelligence systems, perform a comparative analysis of several algorithms.
Special skills:
Is able to choose and construct system architecture of proper parameters to implement artificial intelligence solution.
Is able to create conceptual and graphical models of entities and processes for the analysed system and to select suitable tools and platforms for model development.
Is able to explain optimization theory and to choose appropriate optimization algorithms to solve specific practical engineering problems.
Is able to design hybrid intelligence solutions by applying algorithms of image and speech processing and machine learning.
Is able to deliver software life cycle phases by specifying the requirements, designing, developing, testing and implementing the artificial intelligence solutions, including guaranteeing their quality and reliability.
Is able to apply programming and other practical skills while developing autonomous, self-learning and human-assistance intelligence systems.
Social skills:
Is able to present ideas and decisions in written and oral form for various audiences and to communicate in at least one foreign language.
Is able to work in a team as a team member and as a leader.
Personal skills:
Is able to organize work, show initiative and personal responsibility.
Is able to plan self-education, study independently and adopt lifelong learning.
Is able to demonstrate creativity in solving professional tasks and problems.
Activities of teaching and learning:
The studies include classroom work (lectures, practical work, laboratory work, seminars, outgoing visits to enterprises, etc.) and individual work for mastering theoretical material, preparation for classroom work, intermediate and final assessments and performing other activities. The studies of each study module are completed by the assessment of the student’s knowledge and skills – an examination or another final assessment; the study programme is completed by the final degree project and its defence.
Methods of assessment of learning achievements:
The applied cumulative assessment system of the learning outcomes ensures constant and involving work of students during the entire semester of studies; the final evaluation of the study module consists of the sum of the grades of intermediate assessments and the final assessment multiplied by the weighting coefficients (percentages of components).
Study subjects (modules):
Application of Cognitive Neuroscience, Artificial Intelligence Ecosystems, Introduction to Object-Oriented Programming, Introduction to Studies of Informatics, Mathematics 1, Databases, Mathematics 2, Object-Oriented Programming 2, The First Principles of Digital Logic, Academic and Technical Communication in English (Level C1), Computer Architecture, Data Processing and Analysis, Data Structures, Discrete Structures, Algorithms for Big Data Processing, Design and Analysis of Computer Algorithms, Fundamentals of Multi-Agent Systems, Operating Systems, Software Engineering, Computer Networks and Internet Technologies, Fundamentals of Information Systems, Image Processing Algorithms, Machine Learning Algorithms 1, Numerical Methods and Algorithms, Deep Learning, Machine Learning Algorithms 2, Product Development Project, System Simulation, Cloud Computing for Artificial Intelligence, Speech Recognition Algorithms, Bachelor’s Degree Final Project, Professional Internship.
Electives of Philosophy and Sustainable Development: Media Philosophy, Sustainable Development;
Electives: Artificial Intelligence for Gaming, Robot Programming Technologies, Intelligent Assistive Systems Technologies, Business Ethics, Artificial Intelligence in Business Processes.
Distinctive features of a study programme:
During the first year of studying, students deepen their knowledge in programming technologies, designing and analyzing algorithms, fundamentals of software engineering, designing and modelling IT systems. That is, students get knowledge which is necessary for IT specialist of any field and increase their general competences as a developer of software systems and designer of solutions in informatics. Since the second year, students study topics on artificial intelligence, such as data processing and analysis, image processing and analysis, machine learning algorithms, deep learning. They also learn to design and develop AI-based solutions and systems.
While studying, students have opportunity to gain practical experience in leading Lithuanian and foreign companies which implement solutions in software engineering, information technology and artificial intelligence fields.
Access to professional activity:
A graduate can work as AI Solutions Architect, AI Analyst, Intelligence Systems Developer, IT Systems Developer, Developer of Assistance Systems, Intelligence Systems Maintainer.
Access to further study:
The study programme has continuity in all three study cycles (Bachelor’s, Master’s, Doctorate). After graduating the programme, AI specialist has access to the second cycle studies of Informatics, Software Engineering or Information and Information Technology Security. For those who plan to continue studies in the third cycle (Doctorate), the PHD programmes of Informatics and Informatics Engineering are offered at the University.
