Machine Learning

10 credits

Syllabus, Master's level, 1DT071

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Computer Science A1N, Technology A1N
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 27 April 2015
Responsible department
Department of Information Technology

Entry requirements

120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures.

Learning outcomes

After the course, the students will be able to:

  • setup and solve typical machine learning problems, by implementation or by using established computer simulation tools.
  • decide which machine learning methods/algorithms are suitable for which type of learning problems, i.e. know about their most important weaknesses and advantages.
  • decide how to represent data to facilitate learning.
  • recognise typical effects of bad initialisation and parameter selection and suggest ways to improve the results.
  • describe how, and why, machine learning and natural computation methods work, explain principles and show examples.


The course introduces various machine learning techniques, with a focus on natural computation methods. The course is divided into a theoretical part (4 credits) and a practical part (6 credits).

Theoretical part

The theoretical part consists of lectures and written course material on various topics, including (but not limited to)

Learning paradigms (supervised, unsupervised and reinforcement learning).

Artificial neural networks for classification, function approximation and clustering.

Reinforcement learning methods and Temporal Difference Learning.

Evolutionary computing (genetic algorithms, genetic programming).

Swarm Intelligence (ant colony optimisation, particle swarm optimisation).

Practical part

The practical part consists of lab assignments and a project assignment. The subject of the project assignment is open for the students to define themselves, but must be approved before the work begins.


Lectures, lab assignments and a self-defined project assignment.


Written or oral exam, and through written reports covering the lab assignments and the project assignment. The students are also required to present their results from the project assignment in seminar form.

Other directives

This course cannot be included in the same degree as 1DT022 or 1DT646.