Artificial Intelligence
Program Overview
The relevance of a master's program in Artificial Intelligence is driven by several factors. One of the main factors is the global trend. Today, leading universities and technology companies around the world are actively investing in AI research and education.
The master's program in Artificial Intelligence aims to equip students with in-depth knowledge of the theoretical foundations, methodologies, and modern technologies of artificial intelligence. The program covers key areas such as machine learning, deep learning, neural networks, intelligent systems, natural language processing, intelligent data analysis, and the ethical aspects of AI use.
Students will acquire skills in designing, building, evaluating, and adapting AI systems for various professional and research contexts. The curriculum combines theoretical training with practical work, project-based learning, and research activities. Graduates of the program will be able to overcome challenges in the field of artificial intelligence, promote technological innovation, and develop and apply AI systems in both industrial and academic environments while adhering to ethical standards.
As part of the program, students will study the theoretical foundations of artificial intelligence, the latest technological methods, and their practical applications. The content of the program is tailored to both scientific-research and industrial needs, ensuring high competitiveness of graduates in both local and international job markets. The learning process incorporates modern approaches and research experience, which fosters students' professional growth and the development of innovative skills
Goals, Outcomes & Methods
I. The student will master the theoretical foundations and modern approaches of Artificial Intelligence, including machine learning, deep learning, neural networks, computational models, and intelligent data analysis.
II. The student will plan and implement research-based projects involving data acquisition, preprocessing, modeling, and analysis.
III. The student will be able to evaluate, optimize, and adapt AI systems in various contexts from technical, functional, and applied perspectives.
IV. The student will study in depth the ethical, legal, and social aspects of AI applications in order to develop responsible solutions for real world practice.
V. The student will develop research, collaboration, and self-development skills within a professional framework.
I. Describes, explains, and analyzes the fundamental concepts, algorithms, and models of Artificial Intelligence.
II. Plans, develops, and evaluates AI systems using appropriate modern software platforms.
III. Identifies the basic principles of data analysis and applies them in the processes of data acquisition, processing, and interpretation.
IV. Applies modern research methods and conducts experiments while adhering to professional and academic ethical standards.
V. Evaluates the impact of AI technologies on individuals and society and makes ethically informed decisions in the development of AI systems.
VI. Presents research results in a structured and well-argued manner, both in written and oral form, to academic and professional audiences.
Lectures, seminars, guided discussion, case and problem-based learning, individual and group assignments, presentations, practical or laboratory sessions, independent study, digital learning resources, and project or research supervision.
Program Structure & Plan
The duration of the master’s Program in Artificial Intelligence 2 academic years (4 semesters) and implies the accumulation of 120 ECTS, which equals to 3000 hours. Each credit (ECTS) equals to the learning activity of a student (student workload) of 25 hours and includes both – contact and independent hours.
The distribution of credits among the different study components should be based on a realistic assessment of the study load of a student with average academic achievements that are required to achieve the learning outcomes and goals set for each component.
When calculating the credit, the time determined for the additional exam (preparation, passing, evaluation) as well as the consultation time with the person implementing the component of the educational programme should not be taken into account.
The full workload of an academic year includes 60 (ECTS). During the academic (spring and autumn) semester the student must cover on average 30 credits.
Taking into account the features of the higher education programme and/or the student's individual curriculum, it is allowed for the student's study load to exceed 60 credits or be less than 60 credits during one academic year. It is not allowed for a student's study load to exceed 75 (ECTS) credits in one academic year.
An academic week is a period of time over which the study load of a student with average academic achievement is distributed and includes a combination of activities to be performed during both contact and independent hours.
A semester is a period of time that includes a combination of academic weeks, a period of conducting an exam/additional exam and evaluation of student’s learning outcomes.
The program is regarded as completed, when the student accumulates at least 120 ECTS, which implies the fulfilment of the basic, elective and free components of the field determined under the program.
Course Syllabus
View the course syllabus in a new Univs tab.
Tuition & Fees
| Fee Type | Year 1 | Year 2 | Total |
|---|---|---|---|
| Academic Fees | |||
|
Tuition Fee
|
7,450 | 7,450 | 14,900 |
|
Application Fee
|
300 | — | 300 |
| Living & Accommodation | |||
|
General Living Expenses (Including Accommodation)
|
3,500 | 3,500 | 7,000 |
| Other Fees | |||
|
Visa Fee
|
25 | — | 25 |
|
Residence Permit (TRC) Fee
|
100 | — | 100 |
| Grand Total | 11,375 | 10,950 | 22,325 |
No scholarships are currently available for this program.
Who Should Apply
A graduate of the master's program in Artificial Intelligence possesses both theoretical and practical knowledge of modern AI algorithms and systems and is prepared to work in various positions across both the public and private sectors. The graduate is capable of working as a data analyst, AI or machine learning engineer, data scientist, R&D specialist, software architect, or researcher. Their expertise includes the application of areas such as predictive modeling, computer vision, natural language processing, and more.
Admission Requirements
Passport, Passport-size photo, Academic transcript, Diploma / certificate
Required Documents
Career Prospects
University Accreditation & Recognition
Program Accreditation & Recognition
Program offered within Georgian National University SEU's authorized higher-education framework. Applicants should verify the current program-level accreditation status and any profession-specific recognition with the university and relevant regulator.
Students who require a visa to study at an authorized Georgian institution normally apply for a D3 immigration visa. The correct entry route depends on citizenship and legal residence, so applicants must check Georgia’s eConsul eligibility tool and the responsible embassy before applying. Students staying long term may also need a study residence permit after arrival.
Signed D3 visa application and e-application report
Valid passport
Invitation Letter
Admission letter from an authorized Georgian university
Ministry Order and Rector Order
Proof that the university and programme are authorized or accredited where requested
Proof of accommodation in Georgia
Travel and Health Insurance
Biometric photograph
Visa fee payment receipt
For applicants under 18: parental passport copies and consent letter
10-30 days
20-100
Georgian National University SEU Student Reviews
A preview of verified student reviews and source-attributed reviews about Georgian National University SEU and its programs.
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What I appreciated most was the positive atmosphere she created in class. It made learning much more comfortable and encouraged everyone to participate. Thank you, Dr. Nino, for your dedication and for making this course such a great experience. I would definitely recommend this course to other students.
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What I appreciated the most was how she connected our lessons to real-life situations. She made a maximum effort to give us practical experiences, including taking us to the hospital and even giving us the opportunity to meet one of her patients. These experiences made the subject much more interesting and helped us understand how what we learn can be applied in real life.
She explains topics clearly, cares about her students, and always tries to make the lessons more engaging. I am very grateful to have had her as my professor and truly appreciate all the effort she made for us throughout the course.
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