Artificial Intelligence Technologies
Faculty of information Technology
            Name
        
        
            Artificial Intelligence Technologies
        
    
            Program code
        
        
            21443
        
    
            Qualification awarded
        
        
            Master Computer Science
        
    
            Length of programme
        
        
2 years        
    
            Number of credits
        
        
            120
        
    
            level of qualification according to the National Qualification Framework and the European Qualifications Framework 
        
        
            7
        
    
            Qualification level
        
        
            Second (Master)                                   
        
    
            Discipline
        
        
            Information technologies
        
    
            Speciality
        
        
KnowledgeField EN        
    
            Specific admission requirements
        
        
First cycle of higher education (NQF Level 6) or higher.
        
    
            Specific arrangements for recognition of prior learning
        
        
            
Admission was based on the results of the foreign language common entrance examination and a professional examination
        
    
            Qualification requirements and regulations, including graduation requirements
        
        
            Programme learning outcomes
        
        
            	Apply methodological principles of scientific research; work with a disciplinary array of publications: search, accumulate and process scientific information; choose and formulate a research problem; choose the methodological basis of the study; formulate the object and the subject of research; formulate and test scientific hypotheses; form a set of methods to study the selected subject; collect empirical data; process and interpret empirical data.
	Display the results of scientific research, make scientific reports; participate in scientific discussions; be able to summarize references; present the results of research at scientific conferences and seminars.
	Have the skills to qualify the results of creative activities, protect property and personal non-property rights of authors and owners both in Ukraine and abroad; correctly determine the facts of infringement of intellectual property rights and demand appropriate compensation for damages; enter into contracts with the customer or employer; cooperate with collective copyright administrations; register the intellectual property rights; to file a claim regarding infringed intellectual property rights; to protect infringed intellectual property rights in civil law.
	Define the subject and object of professional ethics; reveal the basic ethical provisions that are important for professional activities in the field of computer science, consider professional ethics as a way to regulate behavior in specific types of professional activities. Define general principles of professional ethics, such as professional duty and special form of responsibility, professional solidarity and corporatism. Analyze the specifics and types of professional ethics in higher education. Reveal the essence of professional deontology and moral codes.
	Apply knowledge of the general principles of didactics, pedagogy, psychology, theory of education from the standpoint of humanization, democratization, national and multicultural orientation of the pedagogical process in higher education.
        
    
            Form of study
        
        
Full-time form        
    
            Examination regulations and grading scale
        
        
            The final evaluation of learning outcomes at the University is carried out on a single 100-point scale. The assessment of the applicant corresponds to the ratio established of the level of professional and general competencies to the planned learning outcomes (as a percentage). The minimum positive level of assessment is 60 points. According to the results of exams, according to the results of course and diploma works (projects); based on the results of work in practice, the student is also graded on a 4-point scale: “Excellent”, “Good”, “Satisfactory”, “Unsatisfactory”. If the final exam is not conducted in the disciplines, the results of the applicant’s work are evaluated on a 2-point scale: “Passed” or “Fail”.
        
    
            Оbligatory or optional mobility windows (if applicable)
        
        
            Work placement
        
        
            Work-based learning
        
        
                    Director of the course
                
                Faculty of information Technology
            Occupational profiles of graduates
        
        
            Computer Systems Analyst, 
University Lecturer,
Junior Researcher (Computing Systems)
        
    
            Access to further studies
        
        
            Access to programmes of the Third Cycle of Higher Education. Has the right to acquire additional qualifications in the adult education system.
        
    Subjects
As part of the curriculum, students study the following disciplines
                        Technologies of computational intelligence
                    
                    
                        Code: ОК.6,
                        
                    
                
                        Business  Analytics
                    
                    
                        Code: ДВС.1.02,
                        
                    
                
                        «Big Data Analysis, Processing, Storage and Visualization.»
                    
                    
                        Code: ОК9,
                        
                    
                
                        Workshop on research aspects of artificial intelligence
                    
                    
                        Code: ОК 5,
                        
                    
                
                        Multiagent Systems and Technologies
                    
                    
                        Code: ОК 11,
                        
                    
                
                        Modeling and Visualization of Multidimensional Data
                    
                    
                        Code: ННД 1.08,
                        
                    
                
                        Scientific research practice
                    
                    
                        Code: ОК 15,
                        
                    
                
                        Course work on artificial intelligence technologies
                    
                    
                        Code: ОК14,
                        
                    
                
                        Graduation qualification work
                    
                    
                        Code: ОК16,
                        
                    
                
                        Methods of optimization and metaheuristics
                    
                    
                        Code: ОК 13,