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INF3490 - Biologically inspired computing

Facts about this course:
Credits:10
Level:Advanced course at bachelor's level
Teaching semester:Every autumn semester
Examination semester:Every autumn semester
Language of instruction:English
Administrated by:Department of Informatics (Ifi)
Detailed course information - Current and previous semesters:

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 outcomes

The candidate should after this course have an overview of algorithms that can be used for intelligent autonomous design and self-adapting applications. This includes both supervised and un-supervised learning for classification applications as well as techniques for search and optimization. Students should be able to learn the benefits and drawbacks of the various methods and what traditional methods that could also be relevant to consider for solving a problem. Through practical assignments, experience will be achieved from both using tools as well as coding your own algorithms.

Admission

Students at UiO must apply for courses in StudentWeb.

International applicants, if you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.

Prerequisites

Formal prerequisites

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.

Recommended prior knowledge

Some experience with programming including the course INF2220 - Algorithms and data structures

Overlap

3 credits against INF5450 - Evolutionary Computing and Evolvable Hardware. 10 credits against INF4490 - Biologically inspired computing.

Teaching

2 hours of lectures and 2 hours of assignment training per week. Mandatory assignments must be completed during the course. Rules for mandatory assignmnets.

Exam information

4 hour written exam. The mandatory assignments must be approved prior the exam. General information about the exam.

Exam resources

No special exam resources are allowed.

Assessment and grading

Course grades are awarded on a descending scale using alphabetic grades from A to E for passes and F for fail. Read more about the grading system .

Possibility of make-up exams and re-takes

This subject does not offer new examination in the beginning of the subsequent term for candidates who withdraw during an ordinary examination or fail an ordinary examination. For general information about new examination, see http://www.mn.uio.no/studier/admin/eksamen/utsatt-og-ny-eksamen/index.html and http://www.mn.uio.no/english/studies/admin/examination/retaking-examinations/

Withdrawing from exams and limits on re-takes

The course is considered equal to INF4490 regarding these exam regulations.

A student can sit for this exam up to 3 times. If a student wishes to withdraw from the exam, s/he must do this in StudentWeb at least two weeks prior to the first day of the exam. Failure to do so will be counted as one of the three opportunities to sit for the exam.

Other information

It is strongly recommended to attend the first lecture since it will be given important information.

Contact us

Department of Informatics (Ifi)

Visiting address: 
Informatics builidng, First floor, room 2316, Gaustadalléen 23

Visiting hours: 
Monday-friday 12:00-15:00

Postal address: 
P.o.Box 1080, Blindern
NO-0316 Oslo

Phone: +47 22 85 24 10
Fax: +47 22 85 24 01
E-mail: 
Web: http://www.mn.uio.no/ifi/english/