DM577: Introduction to Artificial Intelligence

Study Board of Science

Teaching language: English
EKA: N330062102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor

STADS ID (UVA): N330062101
ECTS value: 7.5

Date of Approval: 12-10-2022

Duration: 1 semester

Version: Archive

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/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. The course builds upon competences from DM549 and DM574, and introduces topics that are expanded upon in DM581.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • provide knowledge of a large selection of key algorithms and data structures developed in the field of computer science
  • provide knowledge of knowledge of scientific theory
  • provide the ability to describe, analyse and solve computer science problems by applying methods and modelling formalisms from the core areas of the subject and its mathematical support disciplines
  • provide the ability to analyse advantages and disadvantages of different algorithms, especially in terms of resource consumption
  • provide the ability to make and justify academically related decisions
  • provide the ability to describe, formulate and disseminate issues and results to both peers and non-specialists or partners and users
  • provide competence to understand and reflect on theories, methods and practices pertaining to the subject area of computer science

Expected learning outcome

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

  • outline the basic logic principles of problem solving, reasoning, and decision making;
  • describe in detail the fundamental algorithms of searching, reasoning, and decision making covered in the curriculum of the course;
  • assess the applicability of basic problem solving and reasoning 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


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)




Written exam




Second examiner: Internal


7-point grading scale


Full name and SDU username


Normally, the same as teaching language


3 hours

Examination aids

Not allowed, a closer description of the exam rules will be posted in itslearning.

ECTS value


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 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


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.