Machine Learning


Recommended prerequisite for participation in the module

The module builds on basic knowledge in probability theory, statistics, and linear algebra

Content, progress and pedagogy of the module

The course gives a comprehensive introduction to machine learning, covering learning theory, methods and applications. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives


Students must have knowledge about:

  • Supervised learning methods including logistic regression, support vector machines, and decision trees.
  • Unsupervised learning methods including K-means, Gaussian mixture model, and EM algorithm.
  • Feed-forward, convolutional and recurrent neural networks, transfer learning.
  • Probabilistic graphical models, hidden Markov models.
  • Reinforcement learning.
  • Bayesian decision theory, bias and variance trade-off and cross-validation.


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

Type of instruction

The course will be taught a combination of lectures, demos of applications, exercises and mini-project


Prerequisite for enrollment for the exam

  • Submission of mini-project report


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

Facts about the module

Danish titleMaskinlæring
Module codeESNESK1K1
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
FacultyThe Technical Faculty of IT and Design