Intelligent Interactive Systems
Syllabus, Master's level, 1MD039
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Human-Computer Interaction A1N, Image Analysis and Machine Learning A1N, Technology A1N
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 4 March 2021
- Responsible department
- Department of Information Technology
120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures. Proficiency in English equivalent to the Swedish upper secondary course English 6.
On completion of the course, the student should be able to:
- select appropriate computational techniques and machine learning methods and write programs that use these for automatic detection of human behaviours and states
- determine appropriate design approaches to build social perception abilities such as recognising humans,behaviours and higher level social states and variables (e.g., emotions) based on behavioural features
- describe basic principles of socially adaptive behaviour in robots and embodied interactive systems
- apply basic principles of design and evaluation of human-machine interaction
- evaluate the impact that affect recognition, behaviour detection, and similar technologies may have on ethical values like privacy and autonomy, and to suggest strategies for fulfiling values that are important for users and society at large, including minimisation of negative consequences
Intelligent machines (e.g., robots, virtual agents, user interfaces, smart phones etc.) are increasingly being used to support humans in everyday tasks. This requires them to be able to interact in a safe and social manner with humans in challenging, complex natural environments and to adapt to humans in an intelligent way. This course will provide an overview of computational techniques for intelligent, embodied interactive systems, including approaches for perception (e.g., vision and other modalities of perception), learning and interaction abilities.
Topics include face detection and tracking, facial feature detection and tracking, facial expression and gesture recognition,automatic analysis of multimodal behaviour, automatic inference of affect and social signals, reinforcement learning foradaptive machines, machine embodiment and behaviour generation, control and planning, human-agent and human-robot interaction, ethics in intelligent interactive systems.
Lectures and tutoring.
Written assignments, oral and written presentation of a project.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicatedand allow a student to be assessed by another method. An example of special reasons might be a certificate regardingspecial pedagogical support from the disability coordinator of the university.
No reading list found.