Course - Bio-Inspired Artificial Intelligence - IT3708
IT3708 - Bio-Inspired Artificial Intelligence
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Assignments | 70/100 | |||
| School exam | 30/100 | 90 minutes | D |
Course content
The main focus of the course is to study intelligent systems inspired by the natural world, in particular biology. Several algorithms and methods are discussed, including evolutionary algorithms. Bio-inspired intelligent systems have thousands of useful applications in fields as diverse as computer science, operations research, machine learning, telecommunications, cybernetics, games, music and art. This course discusses both the theory and practice of bio-inspired artificial intelligence, along with providing some of the basis and inspiration for the different approaches.
Learning outcome
Knowledge: The course will give the candidate a general introduction to concepts, methods and algorithms within bio-inspired artificial intelligence. Skills: The candidate will be able to apply actual methods and algorithms. General competence: Through theoretical studies and programming projects the candidate shall understand and apply bio-inspired artificial intelligence, and develop a foundation for the application of bio-inspired artificial intelligence to real-world problems.
Learning methods and activities
Lectures, colloquia, self-study, and projects.
Further on evaluation
Grading is based on a source code handed in by the student and his/her demonstration of the code, and an individual exam.
If there is a complaint against the grade the student has to redo the demonstration.
In the event of voluntary repetition, fail (F) or valid absence, all course-activities must be retaken in a semester with teaching. Exams are only held in semesters with teaching.
Specific conditions
Admission to a programme of study is required:
Computer Science (MIDT)
Computer Science (MTDT)
Industrial Economics and Technology Management (MTIØT)
Informatics (MSIT)
Recommended previous knowledge
The course builds on TDT4120 Algorithms and Data Structures, TDT4136 Introduction to Artificial Intelligence, TDT4171 Artificial Intelligence Methods, and requires previous knowledge in Discrete Mathematics comparable to MA0301 Elementary Discrete Mathematics and TMA4240 Statistics.
Required previous knowledge
The course is only available for students following a specialization in Artificial Intelligence under the programs MTDT, MIDT, MSIT, MTIØT.
Course materials
To be announced.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| MNFIT378 | 7.5 | ||
| MNFIT378 | 7.5 | ||
| IT8801 | 7.5 | AUTUMN 2008 |
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English
Location: Trondheim
- Informatics
- Technological subjects
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD School exam 30/100 D 2024-11-27 09:00 INSPERA
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Room Building Number of candidates - Autumn ORD Assignments 70/100
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Room Building Number of candidates - Spring ORD School exam 30/100 D 2025-05-13 09:00 INSPERA
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Room Building Number of candidates SL111 grønn sone Sluppenvegen 14 22 - Spring ORD Assignments 70/100
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"