Text

Course syllabus - Intelligent Systems

Scope

7.5 credits

Course code

DVA514

Valid from

Autumn semester 2026

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only 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.

    • Ratified date: 2025-11-17
    • Latest update: 2025-11-17

    Books

    Russell, Stuart Jonathan; Norvig, Peter

    Artificial intelligence: a modern approach

    ISBN: 9780132071482 (pbk.)

    LIBRIS-ID: 11712972

    3.,[updated] ed., Boston, Pearson Education, xviii, 1132 s.

    Mandatory

Objectives

This course aims to offer a foundation of intelligent system techniques and their application in various real-world domains and how to implement a system with "intelligent" functionality. Students will learn to judge when intelligent functionality and artificial intelligence may be a good solution for a problem and be able to choose suitable AI methods and techniques. Students will also acquire knowledge enabling them to develop necessary skills to design and implement an intelligent system.

Learning outcomes

After finishing this course the student should be able to:

  1. describe in detail the class of problems that a specific type of artificial intelligence technique and/or algorithm is
    suitable to address,
  2. show the ability to design and implement a prototype in artificial intelligence, furthermore, propose further
    improvements and
  3. show the ability to present a solution both orally and in written form in a short and concise report capturing implementation, results, and conclusions from the project.

Course content

Intelligent agents, fuzzy control, fuzzy adaptive control, multi-sensor data and information fusion, decision analysis with uncertainty, support vector machines, generative artificial intelligence and language models.

Specific requirements

90 credits completed courses in computer science and/or electronics including 15 credits in programming. In addition Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

Seminar (SEM1), 1 credit, examines the learning outcome 3, marks Fail (U) or Pass (G).
Project (PRO1), 6,5 credits, examines the learning outcomes 1-3, marks Fail (U), 3, 4 or 5.

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

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course completely overlaps with DVA406/DVA439 Intelligent systems 7.5 credits.