DM879: Artificial Intelligence

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N340087102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340087101
ECTS value: 10

Date of Approval: 02-11-2022

Duration: 1 semester

Version: Approved - active


Discontinued - offered last time Spring 2024 

1. Exam attempts are held June 2024
2. Exam attempts are held August 2024 
3. Exam attempts will be held in January 2025

Programmes with examination period at the end of the spring semester: If you do not pass the ordinary exam, you can register for re-examination (2. examination attempt) in the same examination period or immediately thereafter, but no later than the last working day of August.

Entry requirements

Students cannot take this course if they have passed DM577, or if DM577 is mandatory in their study profile.

Academic preconditions

The student is expected to have basic understanding of mathematical proofs and programming, obtainable e.g. by having followed DM549 Discrete methods for computer science or MM537 Introduction to Mathematical Methods and DM536/DM550/DM574 Introduction to Programming or DM562 Scientific Programming.

Course introduction

The aim of the course is to equip participants with knowledge about the basic concepts and techniques underlying intelligent computer systems. The focus is on four aspects - problem solving, reasoning, decision making and learning - and on the logical and probabilistic foundations of these activities.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • provide knowledge of a selection of specialized models and methods developed in computer science based on the highest international research, including topics from the subject's research front
  • provide knowledge of computer science models and methods intended for applications in other professional areas
  • provide the ability to describe, analyze and solve advanced computer science problems using the learned models
  • provide the ability to develop new variants of the learned methods where the specific problem requires it
  • provide competence to plan and carry out scientific projects at a high professional level, including managing work and development situations that are complex, unpredictable and require new solutions

Expected learning outcome

At the end of this course, the student is expected to have the following competences:

  • outline the basic logic and probabilistic principles of problem solving, reasoning, learning, and decision making under uncertainty;
  • describe in detail the fundamental algorithms of searching, reasoning, learning and decision making under uncertainty covered in the curriculum of the course;
  • assess the applicability of basic problem solving, reasoning and learning techniques to different problems that resemble those seen in the lectures;
  • develop intelligent systems to solve concrete computational problems.


  • Overview of Artificial Intelligence
  • Search techniques
  • Knowledge, reasoning and planning
  • Probabilistic reasoning
  • Machine learning


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)




Project and written exam




Second examiner: Internal


7-point grading scale


Full name and SDU username


Normally, the same as teaching language


Written exam - 3 hours

Examination aids

Project: To be announced during the course.

Written exam: Without aids. However, it is allowed to use language translation dictionaries (e.g. Danish/English, Danish/German etc) in "ordbogsprogrammet" (the dictionary programme) from in electronic form. The browser version is not allowed. See the complete list of which dictionaries are allowed in the separate "Instruction to ordbogen dot com". All dictionaries other than the allowed dictionaries must be switched off in “ordbogsprogrammet” (the dictionary programme). Internet is not allowed. 

ECTS value


Additional information

The exam consists of a group project and an individual written exam. The written exam includes a number of questions about the project, the aim of which is to evaluate the student's individual contribution to the project. The final grade combines the performance of both components.

Indicative number of lessons

72 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 44 hours
  • Skills training phase: 28 hours tutorials

Activities during the study phase: Solving small assignments, individually or in small groups.

Teacher responsible

Name E-mail Department
Luís Cruz-Filipe Concurrency

Additional teachers

Name E-mail Department City
Marco Chiarandini Data Science


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.