IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
Course description
Course content
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.??
Learning outcome
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
- Others
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
Overlapping courses
- 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).
Teaching
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 approved.?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 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.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
In this course, postponed exams are not offered for exam candidates who are ill before the exam or who become ill during the exam. A deferred submission deadline may be offered. Deadlines may be extended up to 7 days; the duration of the extension depends on the duration of the exam.?The illness must be documented with a doctor's certificate dated no later than the ordinary submission date. You must submit the doctor's certificate to the Student Administration at IFI before the submission deadline for the home exam.
/english/studies/examinations/illness-postponed/index.html
More about examinations at UiO
- Use of sources and citations
- How to use AI as a student
- 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.