- The student can design, develop and evaluate low fidelity and high fidelity prototypes of an intelligent interactive system that is well justified in context.
- The student is able to take real users into account in the analysis, design, and evaluation of interactive systems with respect to both usability and user experience.
- The student can formulate a research question and answer it by choosing and applying various research methods to collect data.
- The student can analyse the collected data by using the appropriate statistical or other methods, and drawing conclusions from the data.
- The student can explain and apply the main AI-techniques concerning logical reasoning, search, Bayesian networks and machine learning.
The Artificial Intelligence (AI) parts gives an introduction into the basic formalisms and methods of AI and the applications of AI. The course offers knowledge and techniques for Search, Logic (building upon Discrete Mathematics in M1), Probabilistic Reasoning, Machine Learning (building upon what was introduced in M1) and applications of AI in Cyber Security. These foundational techniques are widely applicable. In this module they are applied in lab sessions with weekly assignments connected to the AI theory of that week.|
The Human Computer Interaction (HCI) component focuses on the process of designing technology solutions for specific users and specific task domains using a user-centered design (UCD) process. This builds upon the User Requirements topics from M4, adding (among other things) academic skills for qualitative and quantitative user research. Topics include discovery, design, information architecture, research methods, visual design and a general introduction to the field of human-computer interaction. You will learn about techniques of designing, evaluating and prototyping user interfaces, then you will apply those techniques to your own group project.