Level of Study
Objectives and Content
The students should obtain advanced theoretical knowledge and technical skills on the topics covered in the course. The course will enable students to carry out advanced research projects on topics within artificial intelligence and apply the knowledge to complex intelligent system design and development.
The course covers advanced theoretical and technical issues in artificial intelligence. It will focus on some selected topics and applications, such as artificial life, agent and multi-agent systems, machine learning, neural networks, genetic algorithms and programming, data mining, natural language processing, case-based reasoning, cognitive science, and neuro-computing.
A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge: The candidate
- understands advanced theoretical and technical issues in artificial intelligence.
Skills: The candidate
- can apply knowledge for complex intelligent system design and development.
- can carry out advanced research on artificial intelligence.
Recommended Previous Knowledge
Solid background in Informatics/Information Science/Computer Science
Access to the Course
Master in information science. Other students may apply for admission.
Teaching and learning methods
Compulsory Assignments and Attendance
Mandatory participation: Attendance at 80 % of course sessions is mandatory.
Forms of Assessment
Oral individual exam (40%)
Group based assignment(s) (60%)
The grading system has a descending scale from A to E for passes and F for fail.
Assessment in teaching semester
INFO381 is evaluated by students every three years, by the Department every year.
Type of assessment: Semester thesis and one oral exam
- Withdrawal deadline
Exam part: Semester thesis
- Submission deadline
- 14.05.2019, 14:00
- Examination system
- Digital exam
Exam part: Oral exam