autumn 2021
DTE-2502 Neural Networks - 10 ECTS

Last changed 12.05.2022

Type of course

The course can be taken as a single course.

Obligatory prerequisites

DTE-2602 Introduction to Machine Learning and AI

Course content

The course focus on different approaches into the artificial intelligence domain focusing on neural networks. The course is divided into a theoretical part and a practical exercise scheme where the candidates will work in teams to develop and use neural nets in a robotic setting. The candidates will work in groups of 3-4 individuals, assembling the robot, implement navigation and movement based on Kalman or another appropriate AI algorithm. The course focus on topics such as application / programming / understanding of concept of a perceptron, the multi-perceptron, feed-forward neural nets, support vector machines (SVM), recurrent neural networks (RNN), convolution neural nets (CNN) and a basic introduction to deep methods. Emphasis will be placed on applications related to image classification and various forms of regressions.

Recommended prerequisites

DTE-2602 Introduction to Machine Learning and AI, DTE-2605 Programming 1

Objectives of the course

On completion of the course, the successful student is expected to have the following:


The student will have:

  • An overview of history and numerous approaches within machine learning neural nets.
  • Understanding of "The curse of dimensionality" in AI.
  • Basic understanding of back-propagation and complexity.


The student should be able to:

  • Program, adapt and apply neural nets in different application domains.
  • Identify and define features in a complex environment.

General Competence

  • Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.

Language of instruction and examination


Teaching methods

The subject uses so-called "Flipped classroom", i.e., lectures are posted online continuously during the semester in the form of short instructional videos and demonstrations of 10-20 minutes. In addition, exercises and control questions related to each video are used.

The subject teaches in the autumn semester with teacher-led and assistant-led learning and / or exercises. Online students will have access to a teaching assistant for afternoon / evening support.

Information to incoming exchange students

This course is available for inbound exchange students.


There are no academic prerequisites to add this module in your Learning Agreement.


Recommended prerequisites:

DTE-2602 Introduction to Machine Learning and Artificial Intelligence

DTE-2608 Programmering 0

DTE-2605 Programmering 1

Experience in Python programming


Bachelor Level


Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty:


Deadline: 15th April


Course work requirement (work requirements)

  • Mandatory exercises: 4 of 6 approved exercises (Pass / Fail)

Examination and assessment:

Folder assessment with the following content (assessment basis):

  • Mandatory work: 2 programming works (Grade: A-F)
  • Multiple choice test for parts of the syllabus (Grade: A-F)

Work and multiple-choice tests can be weighted differently in their contribution to the final grade in the course.

If the work requirements are not met (delivered and passed at least 4 of 6 submissions) the candidate will not qualify to get a grade in the course.

If more than 30% of the portfolio (assessment basis) is missing (i.e., has passed less than 70% of the portfolio), the candidate will not qualify for continuation and must re-take the course at the next ordinary period.

The portfolio (exercises and tests) can be delivered in Norwegian or English.


Continuation consists of completing the folder within a deadline. The parts of the work requirements that were missing during ordinary deadline will be replaced by new corresponding work requirements that must be made within the agreed deadline.

  • About the course
  • Campus: Narvik | Bodø | Annet | Online |
  • ECTS: 10
  • Course code: DTE-2502