The program is built upon three complementary components: statistical artificial intelligence, symbolic (rule-based) artificial intelligence, and data engineering. Students learn data-driven learning methods such as machine learning, deep learning, and probabilistic models, as well as rule-based approaches such as logical inference, knowledge representation, and ontologies. Neuro-symbolic systems, which combine these two paradigms, are one of the program's distinctive features.
The education is built upon a strong foundation in mathematics and programming; theoretical courses are supported by laboratory work, final projects, and internships. Thus, students are trained as versatile engineers who can evaluate which parts of a problem should be solved using data-driven learning and which parts using rule-based methods.
What are the Educational Goals?
The program aims to equip students not only with the ability to use current tools, but also with an analytical perspective that will enable them to apply different artificial intelligence approaches to the right problems.
Our educational goals include:
- Learning the probability, regression, classification, clustering, and deep learning methods that form the basis of statistical artificial intelligence,
- Within the scope of symbolic artificial intelligence, logical inference, rule-based systems, knowledge representation, and the understanding of ontologies are explored.
- Learning the fundamentals of data engineering, such as data integration, data flow systems, and big data architectures,
- Designing solutions that can use both structured and unstructured data sources together,
- Comparing the strengths and weaknesses of different approaches to an artificial intelligence problem and selecting the most appropriate method,
- The program aims to train individuals who can work in interdisciplinary teams within a framework of ethical principles and a sense of responsibility.
What Qualifications Does It Offer to Graduates?r?
Atlas University's Artificial Intelligence and Data Engineering Department graduates stand out with the following competencies:
- The ability to develop and apply machine learning and deep learning models,
- Proficiency in rule-based systems, information representation, and ontologies.
- The ability to design data engineering solutions that integrate structured and unstructured data.
- Knowledge about hybrid (neuro-symbolic) systems that combine symbolic and statistical methods,
- Knowledge of programming, mathematical modeling, and algorithm design.
- Skills in working in interdisciplinary teams and project management.
- Raising awareness of the ethical, legal, and social dimensions of artificial intelligence applications.
BöWhat facilities does the university have?
The Department of Artificial Intelligence and Data Engineering offers its students a strong technical foundation and practical opportunities:
Applied Laboratories: Well-equipped computer labs,
Research Opportunities: The Center for Brain Research and Applied Artificial Intelligence offers research internship and project management opportunities.
Champagneörel Collaborations and Internships: Atlas University Career Center offers internship opportunities in public, private sector and university R&D centers.
International Programs: Erasmus+ mobility offers training and internship opportunities in artificial intelligence laboratories and technology companies in Europe.
Career and Mentorship Supporti: Career counseling and individual guidance for post-graduate professional development.
In Which Fields Can Graduates Work?
Engineers who understand both the statistical and symbolic aspects of artificial intelligence are increasingly sought after in the industry. Our graduates can pursue careers in many fields where data and intelligent systems are central to decision-making processes.
Positions: Deep Learning Engineer, Rule-Based AI Engineer, Hybrid AI Engineer, Data Engineer, Ontology Specialist, AI Consultant, Research Assistant
Champagneörler: Finance and banking, health and biotechnology, defense industry, energy and transportation, e-commerce, game development, smart city and automation projects, and academic research institutions.
Job opportunities: Opportunities include working at national and international technology companies, remote work models, entrepreneurship, consulting, research and development activities, and an academic career.
Our graduates, with their ability to apply different artificial intelligence approaches to the right problems, are among the key actors in digital transformation.