IN9495 ¨C Advanced Topics in Artificial Intelligence for Intelligent Systems

Course content

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.

Learning outcome

After taking the course, you will:

• have insight into some new and promising methods (within e.g. evolutionary computation, neural networks, swarm intelligence) used in artificial intelligence (AI) and machine learning (ML)
• have some 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.
• to some extent 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 some 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:

1. PhD candidates who have the course approved in their study plan and who will do research including AI/ML
2. Master students in the program Informatics: Robotics and Intelligent Systems (I:RIS)
3. 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
4. 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
5. Master students at the Department of Informatics
6. Others

Recommended previous knowledge

IN3050 ¨C Introduction to Artificial Intelligence and Machine Learning/IN4050 ¨C Introduction to Artificial Intelligence and Machine Learning, INF3490 ¨C Biologically inspired computing (continued)/INF4490 ¨C Biologically Inspired Computing (continued) or similar

Overlapping courses

• 5 credits overlap with IN5490 ¨C Advanced Topics in Artificial Intelligence for Intelligent Systems.
• 5 credits overlap with IN9490 ¨C Advanced Topics in Artificial Intelligence for Intelligent Systems.

Teaching

The teaching will include lectures, discussions and assigment tasks. The teaching will be organized as one or two 1 week workshop sessions joint with IN5490 (where we will try to take into account possible conflicting lectures in other courses). A part of the course is self-study and tasks to complete before and/or after the weeks with teaching.

80% workshop sessions (lectures, discussions etc) attendance 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

Examination

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
• 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.

Grading scale

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.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.