Print Course syllabus
Course syllabus - Artificial Intelligence
Scope
7.5 credits
Course code
DVA349
Valid from
Autumn semester 2026
Education level
First cycle
Progressive Specialisation
G2F (First cycle, has at least 60 credits in first-cycle course/s as entry requirements)
Main area(s)
Computer Science
Organisation
Department of Computer Science & Engineering
Ratified
2025-01-16
Revised
2025-11-03
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
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Books
Objectives
The aim of this course is to cover theories as well as techniques that are fundamental to artificial intelligence.
Learning outcomes
After completing the course, the student shall be able to:
- describe mile stones of AI and relate them to computer science as well as other fields
- implement software that can use most common AI-problems
- define the size and characteristics of a search space for a given problem and suggest suitable AI algorithm and representation
- successfully apply AI algorithms to problem solving
Course content
- Types of problems, in which AI can be applied and how to apply an AI method to them
- Search algorithms: breath-first, depth-first, iterative deepening, informed search, Greedy-Best-first and A*
- Optimization algorithms: Genetic Algorithms, Differential Evolution, Particle Swarm Optimization and Ant Colony Optimization
- Classification method: Artificial Neural Network
Specific requirements
Data Structures and Algorithms 7.5 credits, Object Oriented Programming 7.5 credits and an additional fulfilled course in computer science at 7.5 credits that emphasise programming skills, for example Operating Systems or Functional Programming.
Examination
Laboratory work (LAB1), 1,5 credits, examines the learning objectives 1-4, marks Fail (U) or Pass (G).
Laboratory work (LAB2), 1,5 credits, examines the learning objectives 1-4, marks Fail (U) or Pass (G).
Laboratory work (LAB3), 1,5 credits, examines the learning objectives 1-4, marks Fail (U) or Pass (G).
Laboratory work (LAB4), 1,5 credits, examines the learning objectives 1-4, marks Fail (U) or Pass (G).
Laboratory work (LAB5), 1,5 credits, examines the learning objectives 1-4, marks Fail (U) or Pass (G).
A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.
Grade
Two-grade scale
Interim Regulations and Other Regulations
The course completely overlaps with CDT312/DVA340 Artificiell intelligens, DVA251/DVA264 Artificiell intelligens 1 and DVA255/DVA265 Artificiell intelligens 2.