Artificial Intelligence


Artificial Intelligence (AI) can be described as the part of computer science that aims at studying and building intelligent systems (or machines). A system can, for example, be considered intelligent if it thinks or acts like a human in a given situation, or if it is able to perform tasks that normally requires human intelligence. One way to determine whether a system is intelligent is to apply the so-called turing test. The turing test suggests that a machine (system) is intelligent if a person who communicates with it is unable to determine whether it is a machine or a person.

A clear trend in society is that the task we wish to delegate to computer systems is getting more and more advanced; nowadays, it is common that we want computers to be responsible for tasks that normally require intelligent human behavior, such as controling an airplane. AI can therefore be found more and more in the systems we use, for example, in the form of opponents in computer games, decision support systems based on advanced optimization and data mining, and autonomous robots.

In order to understand and develop the current and future computer systems, and to approach advanced problems using computers, it is important to develop a basic understanding of, and ability to apply, concepts and methods found in the AI field. This course aims to support the development of these abilities.

Admission requirements

  1. The equivalent of English 6/English B in Swedish secondary school.
  2. At least 30 credits in Computer Science, including 15 credits of Object Oriented Programming.


Syllabus for students autumn 2015

Course Code:
DA272A revision 1
Swedish name:
Artificiell intelligens
Level of specialisation
Main fields of study:
Computer Science
Date of ratification:
05 February 2015
Decision-making body:
Faculty of Technology and Society
Enforcement date:
31 August 2015

Entry requirements

  1. The equivalent of English 6/English B in Swedish secondary school.
  2. At least 30 credits in Computer Science, including 15 credits of Object Oriented Programming.

Specialisation and progression relative to the degree regulations

This course is included in the main field of Computer Science at level 31-60 credits, and is offered as elective or single subject course.


The aim of the course is to introduce the field of Artificial Intelligence (AI), as well as the basic concepts and techniques that are used within the field. In addition, the course will develop insights into some of the application areas where artificial intelligence plays an important role.


The course contains the following parts:

  • Introduction to AI
  • Agent technology
  • Problem solving (including search methods)
  • Knowledge representation and logic
  • Machine learning
  • Applications

Learning outcomes

Knowledge and understanding
On completion of the course the student shall:

  • demonstrate understanding of AI and of the basic concepts and methods that are included in the field, as well as being able to show knowledge within the field
Skills and abilities
On completion of the course the student shall:
  • demonstrate ability to implement AI-based solution methods in order to approach problems that are suitable to solve using these types of methods; both individually and together with others
Judgement and approach
On completion of the course the student shall:
  • demonstrate ability to suggest AI-based solution methods for problems that are suitable to approach using AI, as well as being able to assess the suitability of different methods
  • show ability to identify, formulate, and categorize problems that are suitable to approach using different types of AI-based methods

Learning activities

Lectures approximately 30 hours, instructor-led computer labs approximately 12 hours and individual studies approximately 158 hours.


Requirements for Pass: Passed on written examination 3 credits and passed on all lab examinations 4,5 credits.

Requirements for Pass with distinction: Pass with distinction on the written examination and passed on all lab examinations.

Grading system

Fail (U), Pass (G) or Pass with Distinction (VG).

Course literature and other teaching materials

  • Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. 3.,[updated] ed. Boston: Pearson Education. ISBN-10: 0136042597
  • Witten, Ian H., Frank, Eibe & Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques. 3. ed. Burlington, MA: Morgan Kaufmann. ISBN-10: 0123748569
  • Wooldridge, Michael J. (2009). An introduction to multiagent systems. 2nd ed. Chichester, U.K.: John Wiley & Sons. ISBN-10: 0470519460

Course evaluation

The university provides all students who are participating in, or have completed, a course to express their experiences and views on the course through a course evaluation which is organized at the end of the course. The university will collate the course evaluations and provide information about their results and any actions prompted by them. The results shall be made available to the students. (HF 1:14).

Interim rules

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

Other Information

The following generic skills are practised in the course:
• Problem Solving
• Ability to work in a team
• Ability to present work orally and in writing


The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information

Johan Holmgren, Course Coordinator
Phone: 040-6657688
Bodil Sterner, Student Administrator
Phone: 040-6657620


09 November 2020 - 17 January 2021 Day-time 50% Malmö Schedule Application code: mau-08330 This course is offered as part of a program

Tuition fees

for non-EU students only

First instalment: 16000 SEK
Full tuition Fee: 16000 SEK

Open for late application