IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
The course covers various methods within artificial intelligence (AI) and machine learning (ML), and their applications. Examples include algorithms for search, optimization and classification, which to a large extent consist of bio-inspired approaches. Examples of relevant applications include robotics, music, and health and medicine. The course syllabus will continuously be updated with methods from state-of-the-art research. The content is based on presentations from ROBIN group staff, the course participants and invited guests, and will vary depending on who is involved. Students will explore the scientific literature on a selected topic of their interest and proceed to own implementation and conducting experiments under supervision of a ROBIN staff member. The experiments will be disseminated through writing a paper and giving presentations.
After taking the course, you will:
- have insight into novel methods (within evolutionary computation, neural networks, swarm intelligence) used in artificial intelligence (AI) and machine learning (ML)
- have knowledge about how to use AI and ML for various applications
- be able to search for literature outlining state-of-the-art within a specific research field.
- be able to critically assess scientific papers and be familiar with the structure of a scientific paper
- be able to design and conduct experiments using AI and ML, with emphasis on evaluation
- have experience in presenting scientific work to others
- have experience in writing a scientific paper
Admission to the course
The course is limited to 25 students (25 in IN5490 and 5 in IN9490, 30 in total). If the number of enrolled students is higher than the limit, they will be ranked as follows:
- Master students in the program Informatics: Robotics and Intelligent Systems (I:RIS) with the Robotics and Intelligent Systems programme option.
- Master students at the Department of Informatics who (will) have the course approved in their study plan and will do master thesis research including AI/ML
- Master students at the Faculty of Mathematics and Natural Sciences who (will) have the course approved in their study plan and will do master thesis research including AI/ML
- Master students at the Department of Informatics
Recommended previous knowledge
IN3050 – Introduction to Artificial Intelligence and Machine Learning/IN4050 – Introduction to Artificial Intelligence and Machine Learning, INF3490/INF4490 – Biologically Inspired Computing (continued) or similar
- 10 credits overlap with IN9490 – Advanced Topics in Artificial Intelligence for Intelligent Systems.
- 5 credits overlap with IN9495 – Advanced Topics in Artificial Intelligence for Intelligent Systems (continued).
The course material is taught through lectures, discussions, supervision, and assignments. Lectures and student presentations will be organised as two- or three-week workshop sessions. Teaching is combined with IN9490. Project work is done in groups in between the workshop weeks and will be guided by a group supervisor who is an expert on the chosen topic.
80% attendance of workshop sessions (lectures, discussions etc) is required and the students must be active in discussions and give at least one paper summary presentation and one or more presentation(s) of their project. There are mandatory assignments/tasks that must be passed Mandatory assignments and other hand-ins at Department of Informatics
To pass, the following requirements need to be fulfilled throughout the semester:
- Students must give at least one paper summary presentation.
- Prepare one paper draft (in a group) and get approved compulsory assignments. Equal contribution of each project member in a group is expected, otherwise, additional assignments may have to be passed.
- Attend at least 80% of all seminar sessions.
It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN9490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
Language of examination
The examination text is given in English, and you submit your response in English.
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.