Intelligent Interactive Systems (IISY)

This English-taught Master of Science programme trains you to design, analyse and build advanced cognitive systems — software and robots that perceive complex, changing environments and interact with them intelligently. A central theme is learning: students work with methods that allow systems to adapt to new tasks, users or unknown conditions. The curriculum reflects contemporary advances in intelligent systems that operate autonomously in real-world settings.

The programme emphasizes both scientific foundations and hands-on competence. You will develop the theoretical grounding in machine learning and cognitive computer science needed for independent research, while also acquiring practical, professional skills for developing state-of-the-art applications. Teamwork, critical thinking and interdisciplinary research methods are explicit components of the training.

Graduates are prepared for roles across research and industry where adaptive, interactive systems are needed. Typical application domains include robotics and autonomous agents, assistive technologies, recommender systems and web information systems — all areas where systems must perceive, decide and learn in open environments.

Key skills and competencies you will gain

• Analyse, design and develop cognitive and interactive systems that perceive and act in complex environments
• Apply machine learning and cognitive computer science principles to enable system adaptation and learning
• Conduct independent, scientifically grounded research and development
• Practical engineering and professional skills for building deployable systems
• Interdisciplinary research methods, critical thinking and effective teamwork
• Experience relevant to application areas such as robotics, autonomous agents, assistive systems, recommender systems and web information systems

Curriculum overview

The programme is organised around three specialist tracks—Interaction Technology (INT), Artificial Intelligence & Machine Learning (AI), and Autonomous Systems Engineering (ASE)—allowing you to tailor your Master’s to your interests. In the first semester you build or strengthen core skills through basic modules drawn from these thematic blocks. Specialisation intensifies in semesters two and three via advanced (theoretical/methodological) and applied (hands‑on) modules, chosen from the course offerings in your selected block(s).

Modules are offered in two depths: standard (5 credit points) or in‑depth (10 credit points), so you can choose breadth or greater depth in particular topics. Across the programme you will develop both theoretical understanding (e.g., methods in AI, interaction theory, control and perception for autonomous systems) and practical competencies (e.g., implementational and experimental skills). The course selection model supports interdisciplinary study across INT, AI and ASE.

Research skills and teamwork are emphasised. A Scientific Research Methods (SRM) module in the first and second semesters teaches the scientific techniques required to plan and complete the Master’s thesis. In the third semester you work in a small group project (10 credit points; teams of three to five). That project is designed for students from different specialisations to independently plan and execute practical work; assessment records team performance and individual contributions separately and yields an individual, non‑graded module performance evaluation.

Requirements (key programme features)

• Three thematic blocks available: Interaction Technology (INT), Artificial Intelligence & Machine Learning (AI), Autonomous Systems Engineering (ASE)
• First semester: basic modules from the blocks to acquire or extend foundational competencies
• Second & third semesters: specialisation via advanced (theoretical-methodical) or applied (practical) modules
• Module sizes: standard = 5 credit points, in‑depth = 10 credit points
• Choice of courses for each module type from the respective block’s offerings
• Scientific Research Methods (SRM) module in semesters 1 and 2 to prepare for the Master’s thesis
• Third‑semester group project (10 credit points; 3–5 students); assessed for team and individual contributions, resulting in an individual, non‑graded performance evaluation

Graduates are prepared for technical and research roles in industries that develop intelligent interactive systems — for example in robotics, autonomous systems, assistive technologies, recommender systems and intelligent web services. The combination of theoretical foundations and applied project experience suits positions in R&D groups, industrial development teams and technology-focused startups.

The programme’s emphasis on scientific research methods and machine learning also provides a strong basis for continuing to doctoral studies or research careers in academia and research institutes, should graduates choose that path.