Academic communication is carried out through formally defined institutional channels. Each student is assigned an academic advisor at the time of enrolment. Guidance on course selection, monitoring of academic progress, internship planning and post-graduation orientation is provided within the scope of this advisory relationship.
Faculty members announce their weekly office hours at the beginning of each semester. Student views on course content and delivery are collected through course evaluation surveys, class representatives and feedback submitted to the department boards.
Project courses, laboratory sessions and the graduation project are courses in which students work under the supervision of a faculty member. The research interests and contact details of our faculty members are available under the academic staff section of the department website.
Our department is a newly established program and, as of 2026, does not hold MÜDEK accreditation. MÜDEK evaluates only those programs that have produced their first graduates; for this reason, no newly opened engineering program can apply for accreditation within its first four years.
Once our program has produced its first graduates, the application for MÜDEK evaluation is planned to be submitted upon the completion of the program’s fifth year.
The absence of accreditation at this stage does not affect the validity of the diploma. MÜDEK is a program evaluation and quality certification system that is separate from the official validity of an engineering diploma; the validity of the diploma is determined under the legislation of the Council of Higher Education (YÖK).
Our department offers a double major program with the Computer Engineering Program of Ankara University. A double major allows students who meet the required academic conditions to earn a second undergraduate diploma. A minor, by contrast, does not lead to a second diploma; it is a certificate program awarded when a student completes a defined set of courses in another field in addition to their own program.
The principles set out for minor programs in our university’s 2026 Double Major and Minor Guidelines are as follows:
- Undergraduate students may apply no earlier than the beginning of the third semester and no later than the beginning of the sixth semester.
- A cumulative grade point average of at least 2.50 / 4.00 is required to apply.
- In addition to the credits required for graduation from the main program, at least 45 ECTS credits of coursework must be completed successfully, excluding shared courses.
- A cumulative grade point average of at least 2.00 is required to complete the program. If the average in the main program falls below 2.50, the minor registration may be affected.
The success ranking and grade point average requirements for double major applications are set out separately in the same guidelines. The programs open to double major or minor study, the quotas allocated and any additional requirements are announced by our university in each application period.
Foundation years
Introduction to Programming, Mathematics, Physics, Data Structures and Algorithms, Linear Algebra, Discrete Structures, Algorithm Analysis, Object-Oriented Programming, and Probability and Statistics for Data Science are taken during the first two years.
Specialization courses
In the following years, students take Machine Learning, Advanced Artificial Intelligence, Software Engineering for Artificial Intelligence, and Deep Learning.
Technical elective pool
Elective courses are offered in areas such as database management systems, image processing, big data analytics, data mining, graph machine learning, computer vision, natural language processing, optimization, data visualization and autonomous systems. In the fourth year, the internship together with research and project oriented courses form part of the program. Whether a given elective is offered depends on sufficient student demand and the assignment of teaching staff in the relevant semester; the list of electives offered each semester is announced during the course registration period.
The curriculum is treated not as a fixed list of courses but as a whole whose learning outcomes are monitored and updated where necessary. Program outcomes, course learning outcomes, teaching methods, and feedback from students and external stakeholders are used in program development within the department and faculty quality processes. The current course plan and course contents are published on the department website.
Both programs share a common foundation in programming, data structures, algorithms, databases and software engineering. The difference lies in what is built on that foundation: Computer Engineering covers a broad range of computer systems, whereas Artificial Intelligence and Data Engineering concentrates on data, statistics, machine learning and artificial intelligence systems at an earlier stage of the curriculum.
| Criterion | Computer Engineering | Artificial Intelligence and Data Engineering |
| Main focus | A broad field of computer systems covering software, algorithms, operating systems, networks, hardware and architecture, databases, embedded systems and artificial intelligence. | Data science, probability and statistics, machine learning, deep learning, natural language processing, computer vision, big data and data infrastructures, built on a programming and algorithms foundation. |
| Systems and hardware in compulsory courses | Carry greater weight; operating systems, computer architecture and networks are generally part of the compulsory core. | Carry less weight; systems and hardware topics are largely taken as electives. |
| Start of field-specific courses | Specialization usually becomes distinct from the third year onwards through elective courses. | Compulsory courses based on data and learning appear in the curriculum from the second year onwards. |
| Career fields | Software development, systems, data, artificial intelligence and machine learning, and other areas of information technology. | Artificial intelligence and machine learning, data engineering, data science, MLOps and software development. |
The table summarizes the general differences between the two types of program; course plans vary from one university to another. When making comparisons, the current course plans of the universities concerned should be examined.
The fields in which graduates work overlap considerably in the labour market. A graduate of Artificial Intelligence and Data Engineering is not confined to artificial intelligence, nor is a Computer Engineering graduate confined to software development. Alongside the courses taken, the projects completed, internship experience, level of programming and algorithmic skill, and foreign language proficiency are decisive in the field a graduate enters.
Graduates of the program may pursue positions focused on artificial intelligence and data. The following list indicates common job descriptions in the field:
- Artificial intelligence and machine learning engineering
- Data engineering
- Data science
- MLOps and machine learning platform engineering
- Computer vision and natural language processing engineering
- Data analytics and business intelligence
- Software development and backend development
- Research and development engineering
These roles are found in defence and aerospace, finance and banking, e-commerce, telecommunications, healthcare and bioinformatics, manufacturing, consultancy, public institutions and technology companies. Because data engineering covers the collection, cleaning, storage and reliable processing of the data on which artificial intelligence models depend, it constitutes a skill set that transfers across sectors.
Some of the roles listed, particularly research-oriented ones, may require graduate study. Graduates may apply to master’s and doctoral programs in artificial intelligence, data science, computer engineering and related fields, in Türkiye and abroad; an academic career follows from this path.
To prepare students for employment after graduation, the program includes a compulsory internship, project courses and a graduation project as part of the curriculum.
The official language of instruction in our department is Turkish. There is therefore no compulsory English preparatory year; students begin their studies directly in the first year.
Our university nevertheless offers an optional foreign language preparatory program. Students who wish to improve their language proficiency may apply during the enrolment period. Application requirements, placement and proficiency examinations, and attendance rules are announced by our university for each academic year.
The curriculum also includes Basic Foreign Language courses. Since a substantial part of the technical documentation, academic publications, open-source project resources and job advertisements in this field is in English, students are advised to develop their English proficiency throughout their undergraduate studies. Foreign language proficiency is also among the assessment criteria in Erasmus applications.
An artificial intelligence system consists of more than a model. For a model to work with accurate, current and reliable data, the underlying data infrastructure must also be built to engineering standards. This term in the program title refers to that infrastructure.
In practice, a large part of an artificial intelligence project consists of collecting, integrating, cleaning, validating, storing and transforming data, and maintaining its continuous flow in a production environment. This area intersects with databases, data pipelines, big data systems, data quality, distributed systems, and cloud and data platforms.
The presence of database management systems, probability and statistics for data science, big data analytics, data mining and data visualization in our curriculum shows that the data dimension is not merely a phrase added to the program title. This structure enables students to understand not only model development but also the data infrastructure on which a model runs.
An internship is compulsory in our department, and the course YZM499 Internship appears in the curriculum with 6 ECTS credits. According to our internship guidelines, the internship may be completed at a single workplace over 30 working days. If it is divided between two separate workplaces, each internship must last at least 20 working days, bringing the total to 40 working days.
Students may begin their internship from the end of the fourth semester onwards. With the approval of the Department Internship Committee, an optional internship may also be undertaken in the third and fourth years. Finding an internship placement, the application documents and the assessment procedure are set out in detail in the internship guidelines.
Our university publishes application and result announcements for Erasmus+ student mobility for studies and Erasmus+ mobility for traineeships in each academic year. Our internship guidelines state that an internship abroad may be undertaken through programs such as Erasmus+ traineeship mobility and the International Association for the Exchange of Students for Technical Experience (IAESTE).
In mobility for studies, the universities to which students may apply depend on the bilateral agreements in force for the department in the relevant period and on the announcement published. In mobility for traineeships, the host institution that issues the letter of acceptance, the application requirements and the grant conditions are decisive. Since bilateral agreements, quotas and grant amounts may change from year to year, the announcements of the Erasmus Office apply in the relevant application period.