Intelligent Services, 7.5 credits
Intelligenta tjänster, 7,5 hp
Course code: IK8014
School of Information Technology
Level: Second cycle
Select course syllabus
Finalized by: Forsknings- och utbildningsnämnden, 2024-09-18 and is valid for students admitted for spring semester 2025.
Main field of study with advanced study
Informatics, Second cycle, has second-cycle course/s as entry requirements. (A1F)Entry requirements
The course Services in the Digital Society 15 credits. Exemption of the requirement in Swedish is granted. English 6.
Placement in the Academic System
The course is included in the Master´s programme: Digital Service Innovation 120 credits. The course is also offered as a freestanding course.
Objectives
The aim of the course is for the student to understand the technical logic and the structure behind intelligent services. By allowing students to meet business-level problem formulations, and analysis of the quantity and quality of available data, as well as the selection of appropriate algorithms and the evaluation of results, the aim is for students to gain an understanding of both the possibilities and limitations with the different methods of data mining.
Following successful completion of the course the student should be able to:
Knowledge and understanding
- describe the basic features of digital technology and the underlying digital logic of intelligent services
- describe security risks related to digital technology and society's digitalisation
- identify problem areas that can be addressed with data mining methods
- describe different data mining algorithms and explain their possibilities and limitations
Skills and ability
- formulate data intelligence requirements for intelligent services
- choose the appropriate methods and make reasonable considerations when solving a specific data analysis problem
- use the results of data analysis in the design and evaluation of intelligent services
Judgement and approach
- critically analyse and evaluate the underlying structure of intelligent services from a security, ethical and integrity perspective.
Content
The course looks at the underlying digital technology that digital service innovation is dependent upon. In particular, data mining concepts, algorithms as well as tools for intelligent services are addressed. The course includes a laboratory module in two parts. In the first part, students, working in a group, create a digital service and formulate data analysis that makes it intelligent. In the second part, students go from a given data analysis as a resource to create an intelligent service.
Language of Instruction
Teaching Formats
The teaching consists of lectures as well as laboratory exercises with supervision. Independent projects are done in groups to create an intelligent service using the methods of data mining that are presented in the course.
Grading scale
Examination formats
The examination consists of the following tasks: Written Report and Laboratory report.
1902: Laboratory Report, 2.5 credits
Six-grade scale, letters (FA): Insufficient (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B), Excellent (A)
2001: Written Report, 5 credits
Six-grade scale, letters (FA): Insufficient (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B), Excellent (A)
Exceptions from the specified examination format
If there are special reasons, the examiner may make exceptions from the specified examination format and allow a student to be examined in another way. Special reasons can e.g. be study support for students with disabilities.
Course evaluation
Course evaluation is part of the course. This evaluation offers guidance in the future development and planning of the course. Course evaluation is documented and made available to the students.
Course literature and other materials
Literature list 2025-01-20 – Until further notice
Amershi, Saleema., Weld, Dan., Vorvoreanu, Mihaela., Fourney, Adam., Nushi, Besmira., Collisson, Penny., Horvitz, Eric. Guidelines for human-AI interaction. In Proceedings of the 2019 CHI conference on human factors in computing systems (2019) pp. 1-13)
Engel, Christian., & Ebel, Philipp. Data-driven service innovation: a systematic literature review and development of a research agenda. ECIS 2019 Proceedings. (2019).
Grudin, Jonathan. AI and HCI: Two fields divided by a common focus. AI magazine, 30 (2009) 4, pp. 48-48.
Wärnestål, Pontus. Designing AI-Powered Services. Studentlitteratur, 2022.
Xu, Wei. Toward human-centered AI: a perspective from human-computer interaction. interactions, 26 (2019) 4, pp. 42-46.
Yang, Qian., Steinfeld, Aaron., Rosé, Carolyn., & Zimmerman, John. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 CHI conference on human factors in computing systems. (2020) pp. 1-13.
