Official Syllabus:
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:
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Lecture-seminars
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Demonstration method
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News analysis
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Practical work
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Laboratory work
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Presentations
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Teamwork
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Critical analysis
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Literature review
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Method of working with books
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Searching for relevant materials in electronic format or in the library
Learning outcomes:
Knowledge and understanding
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Differentiates computational tasks and uses basic algorithms and programming principles in the context of their solution;
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Explains key AI technologies and approaches, including machine learning, enhanced learning, natural language processing, and computer vision;
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Explains the importance of ethical and legal frameworks in the creation and application of AI systems;
Skills
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Creates software modules using Python and Java in the context of real-world problem applications.;
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Selects appropriate AI models and evaluates their effectiveness in relation to specific tasks;
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Works effectively in teams, communicates technical information, and presents outcomes at a professional level.
Program Administration
Date of approval: 03-03-2025
Approval protocol number: 13PCD6071-01
Date of program update:
Update protocol number:
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 (Course Titles Only)
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C++ Programming
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Calculus I
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Linear Algebra and Analytical Geometry
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Programming Language Java I
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Introduction to Networks
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Calculus II
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Discrete Mathematics
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Fundamentals of Data Analysis in Python
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Operating System Linux
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Programming Language Java II
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Data Structures and Algorithms
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Probability and Statistics
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Machine Learning
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Java-Based Microservices (Spring Boot)
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Containerization I: Fundamentals of Docker
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Fundamentals of Artificial Intelligence
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AI: Regulation, Ethics, Philosophy
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Natural Language Processing
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Advanced Machine Learning: Reinforcement Learning
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Computer Vision
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Bachelor’s Project
Program Elective (Course Titles Only)
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AI for Cybersecurity
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Integrating Machine Learning into Penetration Testing
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Internet of Things (IoT) & AI
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Modern Approaches in Artificial Intelligence
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MLOps and Data Engineering Mechanisms for AI Systems
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Automated Software Code Development and Delivery through Docker and Jenkins
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Big Data Analysis and Processing with the Hadoop Ecosystem
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Real-Time Data Stream Processing
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Development and Integration of Generative AI Systems
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Containerization II: Docker for AI
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IOS Development
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Non-Relational Databases (MongoDB)
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IT Services and Projects Management
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Operation System
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Computer Architecture
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Introduction to Blockchain and BlockDAG Technologies
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Oracle Database Design and Programming
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Organization of Computer Peripherals
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Internship
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Arduino and Intro to Hardware Security
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Network Infrastructure Essentials: Switching, Routing, Wireless
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Enterprise Networking, Security, and Automation
