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The University Of Georgia
Program name: Artificial intelligence (Eng)
Study Level: Undergraduate
Program leader: Bekar Meladze
Eduard Saakashvili
Study language: English
Qualification: Bachelor of Informatics
Program capacity: 240
Program permission: A prerequisite for admission to the program (for citizens of Georgia): Admission of students to the first level of academic higher education (undergraduate programs) is carried out based on the results of the Unified National Examinations or in accordance with Order №224/N (December 29, 2011) of the Minister of Education and Science of Georgia, upon completion of administrative registration and based on the order of the Rector of the university. A prerequisite for admission to the program (for foreign citizens): Foreign applicants who have received full general secondary education abroad or its equivalent, and/or have studied abroad during the last two years of general secondary education, or students who have lived in a foreign country for the past two or more years and are studying in higher education institutions recognized by the legislation of the host country, may be admitted to the program without taking the Unified National Examinations, in accordance with Order №224/N (December 29, 2011) of the Minister of Education and Science of Georgia. A prerequisite for admission to the program is proof of English language proficiency at the B2 level. This can be confirmed by an internationally recognized certificate (TOEFL iBT – minimum score 72, IELTS – minimum score 5.5, PTE General – minimum level B2, FCE – minimum level B2, CPE – a pass is sufficient; CAE – a pass is sufficient). In the absence of such a certificate, English language proficiency may be confirmed by a university-administered exam, where achieving at least 50% of the total score is considered sufficient. . In the Unified National Exams, it is mandatory for students to pass Georgian and English, as well as one additional subject in mathematics and/or physics, while for foreign students the university provides a mathematics exam, where the minimum score is 35%.
Program goals:

Graduates will have the ability to perform professional tasks in the field of artificial intelligence, such as creating algorithms, data analysis, and intelligent decision-making. They will be able to use programming languages, data structures, and key AI technologies including machine learning, enhanced learning, natural language processing, and computer vision. Students will acquire critical thinking and technical communication skills, work in a team environment, and perform real-task-based projects based on ethical and professional standards. The software provides a theoretical and practical basis for continuing master's studies in artificial intelligence, data science and software.

Methods for Attaining Learning Outcomes:
  • Lecture-seminars;
  • Demonstration method;
  • News analysis;
  • Practical work;
  • Laboratory work;
  • Presentations;
  • Teamwork;
  • Critical analysis;
  • Literature review;
  • Method of working with books;
  • Searching for relevant materials in electronic format or in the library;
Learning outcomes:
Knowledge and understanding
    • Differentiates computational tasks and uses basic algorithms and programming principles in the context of their solution;
    • Explains  key AI technologies and approaches, including machine learning, enhanced learning, natural language processing, and computer vision;
    • Explains the importance of ethical and legal frameworks in the creation and application of AI systems;

    • Skills
    • Creates software modules using Python and Java in the context of real-world problem applications.;
    • Selects appropriate AI models and evaluates their effectiveness in relation to specific tasks;
    • Works effectively in teams, communicates technical information, and presents outcomes at a professional level.
    • Date of approval: 03-03-2025
    Approval protocol number: 13PCD6071-01 
    Date of program update:
    Update protocol number:
    Program details:
    Teaching Process Characteristics:

    To obtain a bachelor's degree, a student needs to earn 240 ECTS, which means completing the core subjects of the program, which includes 132 ECTS (including the bachelor's project of 12 ECTS), and the remaining (108 ECTS) can be accumulated by the student from elective subjects of the bachelor's program.

    Program Core

    Code Subject ECTS Semester
    INFO2117EC++ Programming6 1
    MATH1115ECalculus I6 1
    MATH1123ELinear Algebra and Analytical Geometry6 1
    INFO3230EProgramming language Java I6 2
    INFO9997EIntroduction to Networks6 2
    MATH1166ECalculus II6 2
    MATH3112EDiscrete Mathematics6 2
    AINT1001EFundamentals of Data Analysis in Python6 3
    INFO2214EOperating System Linux6 3
    INFO4250EProgramming language Java II6 3
    INFO4259EData Structures and Algorithms6 3
    MATH2004EProbability and Statistics6 3
    AINT1003EMachine Learning6 4
    AINT1004EJava-Based Microservices (Spring Boot)6 4
    AINT1019EContainerization I: Fundamentals of Docker6 4
    AINT1002EFundamentals of Artificial Intelligence6 5
    AINT1005EAI: Regulation, Ethics, Philosophy6 6
    AINT1006ENatural Language Processing6 6
    AINT1007EAdvanced Machine Learning: Reinforcement Learning6 6
    AINT1008EComputer Vision6 7
    CYBR3030EBachelor's project12 8

    Credits sum:

    132

    Program Elective

    Code Subject ECTS
    AINT1010EAI for cybersecurity6
    AINT1011EIntegrating Machine Learning into Penetration Testing6
    AINT1012EInternet of Things (IoT) & AI6
    AINT1013EModern Approaches in Artificial Intelligence6
    AINT1014EMLOps and Data Engineering Mechanisms for AI Systems6
    AINT1015EAutomated Software Code Development and Delivery through Docker and Jenkins6
    AINT1016EBig Data Analysis and Processing with the Hadoop Ecosystem6
    AINT1017EReal-time Data Stream Processing6
    AINT1018EDevelopment and Integration of Generative AI Systems6
    AINT1020EContainerization II: Docker for AI6
    INFO0111EIOS Development6
    INFO0114ENon-Relational Databases (MongoDB)6
    INFO1108EIT Services and Projects Management6
    INFO2217EOperation System6
    INFO2410EComputer Architecture6
    INFO3011EIntroduction to Blockchain and BlockDAG technologies6
    INFO3252EOracle Database Design and Programming6
    INFO4246EOrganization of computer peripherals6
    INFO4444EInternship6
    INFO5555EArduino and Intro to Hardware Security6
    INFO9998ENetwork Infrastructure Essentials: Switching, Routing, Wireles6
    INFO9999EEnterprise Networking, Security, and Automation6

    Credits sum:

    132









    Matrix Of Privequisites


    Point GPA The university assessment   The general assessment in Georgia
    97-100 4,00 A+ A Excellent
    94-96 3,75 A
    91-93 3,50 A-
    87-90 3,25 B+   Very good
    84-86 3,00 B B
    81-83 2,75 B-  
    77-80 2,50 C+   C good
    74-76 2,25 C
    71-73 2,00 C-
    67-70 1,75 D+   D Satisfactory
    64-66 1,50 D
    61-63 1,25 D-
    51-60 1,00 E E Sufficient
    Not passed
    41-50   FX FX Insufficient
    <40   F F Failed



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