Machine Intelligence


Recommended prerequisite for participation in the module

It is recommended that the student has knowledge of discrete mathematics, algorithms and data structures

Content, progress and pedagogy of the module

Learning objectives


The student should gain knowledge of the following theories and methods:

  • problem solving using search and inference
  • model-based decision making
  • inference under uncertainty
  • learning from experience and learning from data
  • basic techniques and methods in machine intelligence including their theoretical foundations and practical applications


  • use correct technical notation and terminology in writing as well as speech
  • apply basic techniques presented in the course to solve a specific problem
  • explain key principles and algorithms presented in the course


  • be able to evaluate, compare and select techniques and methods within machine intelligence based on a specific problem

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours



Name of examMachine Intelligence
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Contact: The Study board for Computer Science at or 9940 8854

Facts about the module

Danish titleMaskinintelligens
Module codeDSNCSITK121
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Education ownerMaster of Science (MSc) in Computer Science (IT)
Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyThe Technical Faculty of IT and Design