INF3490 – Biologically inspired computing
Course description
Course content
An introduction to self-adapting methods also called artificial intelligence or machine learning. Schemes for classification, search and optimization based on bio-inspired mechanisms are introduced. This includes evolutionary computation, artificial neural networks and more specialized approaches like e.g. swarm intelligence and artificial immune systems. Further, an overview of alternative traditional methods will also be included.
Learning outcome
- An overview of algorithms that can be used for autonomous design and adaptation of intelligent systems.
- Insight in biologically inspired as well as traditional machine learning methods for search, optimization and classification.
- An overview of the benefits and drawbacks of the various methods.
- Knowledge of using the methods for real-world applications.
- Practical assignments with experience being achieved from both using tools as well as coding your own algorithms.
Admission
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
Prerequisites
Formal prerequisite knowledge
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
- Mathematics R1 or Mathematics (S1+S2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies. Read more about special admission requirements (in Norwegian).
Recommended previous knowledge
Some experience with programming including the course INF2220 – Algorithms and Data Structures (continued)
Overlapping courses
- 3 credits overlap with INF5450 – Evolutionary Computing and Evolvable Hardware (discontinued)
- 10 credits overlap with INF4490 – Biologically Inspired Computing (continued)
Teaching
2 hours of lectures and 2 hours of assignment training per week. Mandatory assignments must be completed during the course. Rules for mandatory assignments.
Examination
4 hour written digital exam. The mandatory assignments must be approved prior to the exam.
Examination support material
No examination support material is allowed.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
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.
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
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: INF4490 – Biologically Inspired Computing (continued)
Other
It is strongly recommended to attend the first lecture since it will be given important information.