Chairman’s Welcome Message
Dear Students,
Welcome to our Department of Artificial Intelligence Engineering! At a time when humanity is experiencing one of the most exciting technological transformations in history, it is a great honor and pleasure to have you among us. Artificial intelligence stands at the center of a revolution that is reshaping not only technology but also every aspect of life — from healthcare to finance, from industry to art. You will not merely be followers of this revolution, but pioneers who will help build the future.
Our department aims not only to teach you today’s technologies but also to equip you with the knowledge and skills to design and develop the technologies of tomorrow. To achieve this, we have adopted an educational approach that combines strong theoretical foundations with practical expertise, focusing on core areas such as machine learning, deep learning, data science, natural language processing, and robotics. Our curriculum is enriched with project-based learning methods and hands-on training opportunities in collaboration with industry, preparing you to generate innovative solutions to complex engineering challenges.
During your studies here, under the guidance of our dynamic and expert academic staff, you will not only gain knowledge but also contribute to groundbreaking research that pushes the boundaries of artificial intelligence. Our goal is to nurture engineers who can think analytically, solve problems effectively, work collaboratively, and, above all, remain committed to ethical values and social responsibility. Ensuring that the technologies you develop serve humanity and our nation is our foremost priority.
University life is not only about academic achievement but also a unique journey that shapes your personal development. On this journey, we invite you to be part of an ecosystem that encourages not only learning but also creating, exploring, and pushing boundaries. I am confident that you will make the most of our laboratories, the experience of our academic staff, and all the opportunities our university offers.
I wish you great success on this exciting path as we build the future together.
Warm regards,
Prof. Dr. Gülsüm Aşıksoy
Head of the Department
Artificial Intelligence Engineering Department
Near East University
Courses
1st Semester 2nd Semester 3rd Semester 4th Semester 5th Semester 6th Semester 7th Semester 8th Semester ELECTIVE COURSE
CODE
COURSE NAME
C/E
T
P
C
E
MTH113
LINEAR ALGEBRA
C
3
0
3
5
AII102
PROGRAMMING AND PROBLEM SOLVING
C
3
2
4
5
ENG101
ENGLISH I
C
3
0
2
3
MTH101
CALCULUS I
C
4
0
4
5
PHY101
GENERAL PHYSICS I
C
3
2
4
5
CAM100
CAMPUS ORIENTATION
C
2
0
0
2
CHM101
GENERAL CHEMISTRY I
C
3
2
4
5
Total
21
6
21
30
CODE
COURSE NAME
C/E
T
P
C
E
AII104
DISCRETE STRUCTURES
C
3
0
3
5
ENG102
ENGLISH II
C
3
0
2
3
MTH102
CALCULUS II
C
4
0
4
6
PHY102
GENERAL PHYSICS II
C
3
2
4
6
AII108
OBJECT ORIENTED PROGRAMMING
C
2
2
3
6
CAR100
CAREER PLANNING
C
2
0
0
2
GEC351
21ST CENTURY SKILLS
E
2
0
0
2
Total
19
4
16
30
CODE
COURSE NAME
C/E
T
P
C
E
AII001
LOGIC DESIGN
C
3
2
4
6
AII201
DATA STRUCTURES & ALGORITHMS
C
3
2
4
6
AIE201
AI PRINCIPLES AND TECHNIQUES
C
3
0
3
3
MTH201
DIFFERENTIAL EQUATIONS
C
4
0
4
6
AII007
MULTIMEDIA SYSTEMS
C
3
0
3
5
AIT103
ATATÜRK PRINCIPLES AND REFORMS I
C
2
0
2
2
YIT101
TURKISH FOR FOREIGNERS I
C
2
0
2
2
Total
20
4
22
30
CODE
COURSE NAME
C/E
T
P
C
E
AII202
DATABASE MANAGEMENT SYSTEMS
C
4
0
4
5
AIE206
REASONING AND AGENTS IN AI
C
4
0
4
7
MTH251
PROBABILITY AND STATISTICS
C
3
0
3
6
AIT104
ATATÜRK PRINCIPLES AND REFORMS II
C
2
0
2
2
AIE299
SUMMER TRAINING I
C
0
0
0
6
CHC100
CYPRUS HISTORY AND CULTURE
E
2
0
0
2
YIT102
TURKISH FOR FOREIGNERS II
C
2
0
2
2
Total
17
0
15
30
CODE
COURSE NAME
C/E
T
P
C
E
AII302
OPERATING SYSTEMS
C
3
0
3
6
AIE301
PATTERN RECOGNITION
E
2
2
3
5
ENG201
ORAL COMMUNICATION SKILLS
C
3
0
3
4
AIE303
NATURAL LANGUAGE PROCESSING
E
3
2
3
3
AII439
OCCUPATIONAL HEALTH AND SAFETY I
C
2
0
2
4
AII427
MANAGEMENT FOR ENGINEERS
C
3
0
3
5
AIE204
NEURAL COMPUTATION
C
2
2
3
3
Total
18
6
20
30
CODE
COURSE NAME
C/E
T
P
C
E
AIE302
INTRODUCTION TO MACHINE LEARNING
E
3
2
4
7
AII303
DATA COMMUNICATIONS AND NETWORKING
C
3
2
4
5
AIE304
LEARNING IN HUMANS
E
3
0
3
5
AIE306
DEEP LEARNING
E
2
2
3
5
AII440
OCCUPATIONAL HEALTH AND SAFETY II
C
2
0
3
2
AIE399
SUMMER TRAINING II
C
0
0
0
6
Total
13
6
17
30
CODE
COURSE NAME
C/E
T
P
C
E
AIE401
INTRODUCTION TO ROBOTICS
E
2
2
3
7
AIE403
COMPUTER VISION
E
2
2
3
7
AIE491
SENIOR PROJECT I
C
3
0
3
6
TE
TECHNICAL ELECTIVE
E
2
2
3
5
TE
TECHNICAL ELECTIVE
E
2
2
3
5
Total
11
8
15
30
CODE
COURSE NAME
C/E
T
P
C
E
AII429
ENGINEERING ETHICS
C
3
0
3
6
AIE492
SENIOR PROJECT II
C
4
0
4
7
AIE402
SPEECH PROCESSING
E
2
2
3
7
TE
TECHNICAL ELECTIVE
E
2
2
3
5
TE
TECHNICAL ELECTIVE
E
2
2
3
5
Total
7
0
16
30
Total No. of Courses 51 Total No. of Electives 14 Total No. of Credits 142 Percentage of Electives 27 Total ECTS 240 Previous Total No. of Credits 141 New Total No. of Credits 142 Previous Total ECTS 240 New Total ECTS No change C/E: Compulsory/Elective T: Hours of Theoretical Study P: Hours of Practice/Lab E: ECTS C: Credits
CODE
COURSE NAME
T
P
C
E
AII419
Image Processing
2
2
3
5
AII415
Decision Making
2
2
3
5
AIE411
Advanced Data Analysis
2
2
3
5
AIE412
Information Retrieval and Web Search
2
2
3
5
AIE413
Human-Robot Interaction
2
2
3
5
AIE414
Deep Reinforcement Learning and Control
2
2
3
5
AIE415
Mobile Robot Programming
2
2
3
5
AIE416
Autonomous Agents
2
2
3
5
AIE417
Introduction to Quantum Computing
2
2
3
5
AIE418
Computer Animation & Visualization
2
2
3
5
AIE419
Algorithmic Game Theory and its Applications
2
2
3
5
AIE420
Fuzzy Systems
2
2
3
5
AIE458
Artificial Intelligence and Internet of Things
2
2
3
5
AIE457
AI and Cloud Computing
2
2
3
5
AIE418
Computer Animation & Visualization
2
2
3
5
AIE419
Algorithmic Game Theory and its Applications
2
2
3
5
AIE420
Fuzzy Systems
2
2
3
5
AIE458
AI and IoT
2
2
3
5
AIE457
AI and Cloud Computing
2
2
3
5
AII002
Systems Simulation
2
2
3
5
AII461
Fundamentals of Generative Artificial Intelligence
2
2
3
5
Mission – Vision
The Department of Artificial Intelligence Engineering aims to provide students with a high-quality education that combines theory and practice in fields such as machine learning, deep learning, natural language processing, data science, and robotics, built upon a strong academic foundation in mathematics, computer science, and engineering principles. In this context, our department adopts a research and development-oriented approach in collaboration with industry and academia, aiming to educate engineers who can think critically, possess analytical problem-solving skills, develop innovative projects, and uphold ethical responsibility. Thus, the department’s mission is to nurture individuals capable of competing at an international level and open to interdisciplinary collaboration, guiding the transformation in the field of artificial intelligence.
Program Information
Qualification Awarded
Students who successfully complete the Artificial Intelligence Engineering Bachelor’s Program are awarded the Bachelor of Science (BSc) degree in Artificial Intelligence Engineering and the professional title of Artificial Intelligence Engineer.
Level of Qualification
This is a First Cycle (Bachelor’s Degree) program.
Specific Admission Requirements
Within the framework of the regulations established by the Higher Education Council of Türkiye (YÖK), admission to the undergraduate program is carried out through the national university entrance examination (YKS). Following the submission of academic program preferences by applicants, student placement is conducted by the Student Selection and Placement Center (ÖSYM) based on the scores obtained in the examination.
International student admissions are evaluated based on the results of internationally recognized examinations, such as SAT and ACT, or on applicants’ high school diploma scores, in accordance with the relevant admission criteria.
The admission of exchange students is conducted in line with the terms and conditions specified in bilateral agreements established between the university and partner institutions.
Visiting students may enroll in courses offered within the program upon approval of the relevant academic unit. As the language of instruction is English, visiting students are also required to demonstrate an adequate level of English language proficiency.
Qualification Requirements and Regulations
Students enrolled in the Artificial Intelligence Engineering Undergraduate Program are required to achieve a minimum cumulative grade point average (CGPA) of 2.00 out of 4.00 and successfully complete all courses in the curriculum with a minimum letter grade of DD or S in order to be eligible for graduation. A total of at least 240 ECTS credits must be completed to fulfill the graduation requirements. In addition, students are required to complete the compulsory internship within the prescribed duration and in accordance with the established academic and professional standards.
Recognition of Prior Learning
In accordance with the relevant regulations, students enrolled in the Artificial Intelligence Engineering Program may be granted course exemptions. Courses previously completed at another higher education institution may be considered for exemption, provided that their content and learning outcomes are deemed equivalent to those of the corresponding courses in the Artificial Intelligence Engineering curriculum. Course exemption decisions are made following a formal evaluation process and are subject to the approval of the relevant faculty or graduate school.
Profile of the Program
The Artificial Intelligence Engineering Program is designed to equip students with a strong foundation in the scientific, mathematical, and engineering principles underlying modern computing and artificial intelligence technologies. The program emphasizes the development of analytical thinking, problem-solving skills, and practical competencies required for the design, implementation, and deployment of intelligent systems in real-world applications.
Graduates of the program are well prepared to pursue professional careers in artificial intelligence–related fields across a broad range of industrial sectors, as well as to continue their academic studies at the graduate level. The program aims to educate adaptable and highly qualified professionals who can integrate efficiently into multidisciplinary environments and respond effectively to the rapidly evolving demands of industry and research.
Program Outcomes
Knowledge – Theoretical and Factual Learning Outcomes
- PR1. Has sufficient knowledge in mathematics, natural sciences, and artificial intelligence engineering.
Skills – Cognitive and Practical/Applied Learning Outcomes
- PR2. Identifies, defines, formulates, and solves engineering problems; selects and applies appropriate analytical methods and modeling techniques for this purpose.
- PR3. Analyzes a system, its components, or a process and designs it under realistic constraints to meet desired requirements; applies modern design methods accordingly.
- PR4. Design experiments, conduct them, collect data, analyze results, and interpret them to investigate field-specific research topics.
Competencies – Competency in Autonomous Working and Taking Responsibility Learning Outcomes
- PR5. Ability to work effectively in disciplinary and multidisciplinary teams; ability to work individually.
- PR6. Accesses information and conducts resource research for this purpose; uses databases and other information sources.
Competencies – Learning Competency and Learning Outcomes
- PR7. Is aware of the necessity of lifelong learning; follows developments in science and technology and continuously updates oneself.
- PR8. Selects and uses modern techniques and tools necessary for engineering applications.
Communication and Social Competency Learning Outcomes
- PR9. Is aware of the universal and societal impacts of engineering solutions and applications; recognizes entrepreneurship and innovation issues and has knowledge about contemporary problems.
Competencies – Field-Specific Competency Learning Outcomes
- PR10. Has awareness of professional and ethical responsibility.
Course and Program Outcomes Matrix
Occupational Profiles of Graduates
Graduates of the Artificial Intelligence Engineering program may work as Artificial Intelligence or Machine Learning Engineers and Developers in public and private institutions. Employment opportunities exist in sectors such as healthcare, aviation, cybersecurity, education, marketing, retail and e-commerce, customer services, and financial markets, where graduates contribute to the development and implementation of intelligent, data-driven systems.
Access to Further Studies
The students graduating from this program may apply to graduate programs.
Course Structure Diagram with Course Credits
Exam Regulations, Assessment and Grading
- Midterm and final exams are conducted face-to-face.
- Instructors may additionally use projects, presentations, homework, quizzes, and group work as part of the assessment scheme.
- Assessment criteria and weightings are communicated to students at the start of the semester in the course syllabus.
- The final grade reflects the weighted average of all assessment components applied in the course.
Letter Grade Conversion TableScore Letter Grade Multiplier ECTS Grade 90-100 AA 4.0 A 85-89 BA 3.5 B* 80-84 BB 3.0 B* 75-79 CB 2.5 C* 70-74 CC 2.0 C* 60-69 DC 1.5 D 50-59 DD 1.0 E 49-00 FF 0.0 F Passing thresholds by program level.
To be considered successful in a course: minimum DD for level 5 (associate) and level 6 (undergraduate) programs; minimum CC for level 7 (master’s) programs; minimum BB for level 8 (doctoral) programs. For courses not included in the GPA, students must receive S (Satisfactory).
Additional transcript notations (non-GPA where applicable).
I (Incomplete): Granted when course requirements are not completed by the grade due date for a valid reason accepted by the instructor. All outstanding work must be completed within one week after the grade due date. In exceptional cases, this may be extended to two weeks before the next semester begins by decision of the relevant academic unit administrator and the board. Otherwise, I convert to FF or U.
S (Satisfactory Completion): Assigned for successful completion in non-credit / non-GPA contexts.
U (Unsatisfactory): Assigned for failure in non-credit courses.
P (Successful Progress): For multi-semester requirements not included in GPA when expected progress is achieved.
NP (Not Successful Progress): For multi-semester requirements not included in GPA when expected progress is not achieved.
EX (Exempt): Course exemption granted.
NI (Not Included): Grade recorded but not included in GPA (and not counted within the registered program’s taken courses).
W (Withdrawal): With advisor recommendation and instructor approval, permitted after add/drop and within 10 weeks of semester start. Not allowed in the first two semesters. A student who previously received W in a course that is not included in GPA cannot withdraw from the same course again. Maximum withdrawals: 2 (associate), 4 (undergraduate). Withdrawn courses must be retaken when next offered.
NA (Never Attended): Assigned to students who fail attendance requirements and lose the right to end-of-term assessments. Not included in GPA.
Note: Student transcripts display letter grades corresponding to both national and ECTS credits.
I Incomplete S Satisfactory Completion U Unsatisfactory P Successful Progress NP Not Successful Progress EX Exempt NI Not included W Withdrawal NA Never Attended
Graduation Requirements
To graduate from the Near East University Artificial Intelligence Engineering Bachelor’s Program, students must successfully complete all courses in the curriculum, earning a minimum of 240 ECTS credits with at least a DD/S letter grade, and achieve a Cumulative Grade Point Average (CGPA) of no less than 2.00 on a 4.00 scale. In addition, students are required to complete the compulsory internship(s) specified in the program within the prescribed duration and standards. Upon fulfilling all academic and administrative requirements, the student becomes eligible to receive the Bachelor’s Degree in Artificial Intelligence Engineering.
Mode of Study
This is a full time program.
Program Director (or Equivalent)
Prof. Dr. Gülsüm Aşıksoy, Head of Department, Near East University
Evaluation Questionnaires
- Evaluation Survey
- Graduation Survey
- Satisfaction Survey