Machine Learning


Content, progress and pedagogy of the module

Learning objectives


Key models in machine learning and their associated learning and inference techniques, such as:

  • Statistical linear models
  • Markov chains and hidden Markov models
  • Support Vector machines
  • Neural Net
  • Probabilistic Graphic Models
  • Matrix factorization

The use of machine learning methods in selected fields of application, such as:

  • Web and network mining
  • Recommendation Systems
  • Computer games
  • Image analysis
  • Text mining


  • be able to apply advanced techniques from machine learning to the construction of intelligent systems


  • to understand advanced machine learning methods for designing intelligent systems
  • to analyze their usefulness and impact in solving specific tasks

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 Learning
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 titleMaskinlæring
Module codeDSNDATFK213
Module typeCourse
Duration1 semester
Language of instructionDanish and English
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyThe Technical Faculty of IT and Design