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Syllabus:

Trends in interactive intelligent environments, 7.5 Credits

Swedish name: Trender inom interaktiva intelligenta miljöer

This syllabus is valid: 2019-12-30 and until further notice

Course code: 5DV210

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Computing Science: Second cycle, has second-cycle course/s as entry requirements

Grading scale: TH teknisk betygsskala

Responsible department: Department of Computing Science

Established by: Faculty Board of Science and Technology, 2019-11-08

Contents

The course is divided into two modules:
  • MOM1 Theory and methodology 4.5 hp
  • MOM2 Practical application of theory and methods in a project 3.0 hp
The theoretical module covers theories and methods in Artificial Intelligence (AI) and Human-Computer Interaction (HCI) for designing and constructing intelligent interactive systems and environments. Parts of the module are applied in the practical module, which consists mainly of a project that is conducted individually or in group.
 
Emergent trends in AI aim to combine different fundamental theories and methods within the AI field and across disciplines such as cognitive science and social science to solve societal challenges, and to build new more advanced AI. This course focuses on such trends in interactive intelligent environments that include people and AI-based systems, and where intelligent systems and people pursuing different goals are expected to collaborate. This advanced level course takes a research perspective on theories and methods for interactive intelligent environments, which is done from the perspective of solving societal challenges.
 
The student will deepen her/his knowledge about state-of-the-art in AI topics such as knowledge representation and reasoning, machine learning, natural language processing, multiagent systems and agent societies, computational social norms, ethical AI, and gain knowledge in how these methods and theories can be combined to achieve AI useful for human-AI collaboration in interactive intelligent environments. Moreover, the student will also gain knowledge about theories about human activity that provide frameworks for human-AI collaboration, user modelling, user adaptation and personalisation in e.g., persuasive technology, decision-support systems and digital assistants.
 
The student is encouraged to engage with a research group during the course, and target particular challenging and unsolved issues relating to interactive intelligent environments.

Expected learning outcomes

Knowledge and understanding
After having completed the course the student should be able to:
  • Design and explain architectures for intelligent controllers (rational agents) that are specific for interactive intelligent environments, and that include complementary AI technologies (FSR 1)
  • Design the interactive intelligent environment for human-AI collaboration, and explain how adaptation and personalization are accomplished (FSR 2)
  • Show deepened knowledge and understanding of the possibilities and limitations of existing AI technologies (FSR 3) 
Skills and abilities
After having completed the course the student should be able to:
  • Read and explain scientific articles in the field of interactive intelligent environments (FSR 4).
  • Demonstrate practical skills in developing and evaluating interactive intelligent environments/systems that include intelligent controllers and that collaborate with humans (FSR 5).
Values and attitudes
After having completed the course the student should be able to:
  • Evaluate theoretical and practical results critically and compare these with theoretical, technological and societal (e.g., ethical, safety, security, economical, personal, organizational, etc) expectations (FSR 6)

Required Knowledge

Univ: To be admitted you must have (or equivalent) 90 ECTS-credits including 60 ECTS-credits in Computing Science or three years of completed studies within a study programme (180 ECTS-credits). In both cases, includning
* at least one course (7.5 ECTS-credits) within human-computer interaction, and
* at least one course (7.5 ECTS-credits) on advanced level within Artificial Intelligence, e.g. Artificial Intelligence - Methods and Applications, Interactivity in smart environments, or Machine learning.

Proficiency in English equivalent to Swedish upper Secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Form of instruction

The course consists of lectures, project work in computer labs and other environments, and exercises in small groups. In addition to scheduled activities the course also requires individual work with the material.

Examination modes

Examination of the theoretical module (FSR 1-3) is done through a written examination in halls. The grade on this module is one of the grades Fail (U), Pass (3) or Pass with Merit (4), or Pass with Distinction (5). For all students who do not pass the regular examination there is another opportunity to do the examination.
 
The examination of the practical module (FSR 3-6) is done through completing a project in-group or individually according to instructions provided during the course. Parts of the project work may be done through field studies outside the university in collaboration with societal organisations, and meetings during the project can be located at such organisation. The grade on the practical module is one of the grades Fail (U) or Pass (G).
 
A student that has failed the practical module of the course but has regularly attended a majority of the project activities can be given a re-exam covering the parts that the student has missed. If a student has not participated in the project activities (or missed a majority of them), the student can enroll in the practical part next time the course is given. The student does not have the right to continue with the same project the next time (s)he attend the course, and may need to start over with the project work in collaboration with a new student group and with a new topic.

On the whole course one of the grades Fail (U), Pass (3) or Pass with Merit (4), or Pass with Distinction (5) is given. At least the grade Pass must be achieved on each part in order to get a grade for the whole course. The grade given on the course is the same as the one set on module 1.
 
A student who has passed an examination may not be re-examined. A student who has taken two tests for a course or segment of a course, without passing, has the right to have another examiner appointed, unless there exist special reasons (Higher Education Ordinance Chapter 6, section 22). Requests for new examiners are made to the head of the Department of Computing Science.

Examination based on this syllabus is guaranteed for two years after the first registration on the course. This applies even if the course is closed down and this syllabus ceased to be valid.

Deviations from the examination forms mentioned in this syllabus can be made for a student who has a decision on pedagogical support due to disability. Individual adaptation of the examination forms should be considered based on the student's needs. The examination form is adapted within the framework of the expected learning outcomes of the course syllabus. At the request of the student, the course responsible teacher, in consultation with the examiner, must promptly decide on the adapted examination form. The decision must then be communicated to the student.

Transfer of credits
Students have the right to be tried on prior education or equivalent knowledge and skills acquired in the profession can be credited for the same education at Umeå University. Application for credit is submitted to the Student Services / Degree. For more information on credit transfer available at Umeå University's student web, www.student.umu.se, and the Higher Education Ordinance (Chapter 6). A refusal of crediting can be appealed (Higher Education chapter 12) to the University Appeals Board. This applies to the whole as part of the application for credit transfer is rejected.

Literature

The literature list is not available through the web. Please contact the faculty.