DM879: Artificial Intelligence

Study Board of Science

Teaching language: English
EKA: N340087102
Censorship: 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: 07-10-2020

Duration: 1 semester

Version: Approved - active

Entry requirements


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 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
  • Natural language processing


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

To be announced during the course.

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.

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

56 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 28 hours
  • Skills training phase: 28 hours, including 20 hours tutorials and 8 hours laboratory exercises

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

Teacher responsible

Name E-mail Department
Luís Cruz-Filipe Institut for Matematik og Datalogi, Datalogi, Artificial Intelligence, Cybersecurity, and Programming Languages

Additional teachers

Name E-mail Department City
Marco Chiarandini Institut for Matematik og Datalogi, Datalogi, Datavidenskab & Statistik


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Recommended course of study

Profile Programme Semester Period