Machine Learning


Content, progress and pedagogy of the module

The course gives a comprehensive introduction to machine learning, which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives


  • Must have knowledge about supervised learning methods including K-nearest neighbors, decision trees, linear discriminant analysis, support vector machines, and neural networks.
  • Must have knowledge about unsupervised learning methods including K-means, Gaussian mixture model, hidden Markov model, EM algorithm, and principal component analysis.
  • Must have knowledge about probabilistic graphical models, variational Bayesian methods, belief propagation, and mean-field approximation.
  • Must have knowledge about Bayesian decision theory, bias and variance trade-off, and cross-validation.
  • Must be able to understand reinforcement learning.


  • Must be able to apply the taught methods to solve concrete engineering problems.
  • Must be able to evaluate and compare the methods within a specific application problem.


  • Must have competencies in analyzing a given problem and identifying appropriate machine learning methods to the problem.
  • Must have competencies in understanding the strengths and weaknesses of the methods.

Type of instruction

As described in § 17.



Name of examMachine Learning
Type of exam
Written or oral exam
AssessmentPassed/Not Passed
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleMaskinlæring
Module codeESNSPAK3K2F
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Study BoardStudy Board of Electronics and IT
DepartmentDepartment of Electronic Systems
FacultyTechnical Faculty of IT and Design