IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
Changes in the course due to coronavirus
Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.
The course goes in depth on selected topics and methods within artificial intelligence (AI), machine learning (ML) and their applications. Examples include computational intelligence algorithms in search, optimization and classification, which to a large extent consist of bio-inspired mechanisms. Examples of relevant applications include robotics, music, 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 participants and invited guests, and will vary depending on who is involved.
After taking the course, you will:
- have insight into the new and promising methods (within evolutionary computation, neural networks, swarm intelligence) used in artificial intelligence (AI) and machine learning (ML)
- have knowledge about how to apply AI methods to different kinds of 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 methods, with emphasis on evaluation
- have experience in presenting scientific work for others
Admission to the course
The course is limited to 20 students (IN5490 and IN9495 together). If the number of enrolled students is higher than the limit, they will be ranked as follows:
- Phd students who have the course approved in their study plan and who will do research including AI/ML
- Master students in the program Informatics: Robotics and Intelligent Systems (I:RIS)
- 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
- 5 credits overlap with IN9495 – Advanced Topics in Artificial Intelligence for Intelligent Systems.
The teaching will include lectures, discussions and assigment tasks. The teaching will be organized as one or two 1 week workshop sessions (where we will try to take into account possible conflicting lectures in other courses) potentially including lunches. This part of the teaching is joint with IN9495. Additional sessions will be organized during the semester. A part of the course is self-study and tasks to complete before and/or after the weeks with teaching.
80% attendance of workshop sessions (lectures, discussions etc) is required and the students must be active in discussions and give at least one lecture (about some part of the syllabus). 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 presentation, prepare one paper draft and/or get approved compulsory assignments and attend at least 80% of all seminar sessions.
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.