Entry requirements

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

Progression level in relation to degree requirements

This course is included in the main field of Computer Science.

Course contents

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; both individually and together with others

Judgement and approach

On completion of the course the student shall:

• for a given problem demonstrate ability to suggest AI-based solution methods, 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, seminars, instructor-led computer labs, and individual studies.

Assessment

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.

Course literature

Main literature:

• Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. 3.,[updated] ed. Boston: Pearson Education. ISBN-10: 0136042597
• Wooldridge, Michael J. (2009). An introduction to multiagent systems. 2nd ed. Chichester, U.K.: John Wiley & Sons. ISBN-10: 0470519460
• Collection of articles and chapters

Reference literature:

• 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

Course evaluation

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

Interim rules

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented.

Additional information

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format. The syllabus is a translation of a Swedish source text.