autumn 2024
DTE-3606 Artificial Intelligence and Intelligent Agents- project - 5 ECTS

Type of course

Reserved for students at the Master's Degree Programme in Computer Science.

Admission requirements

A relevant undergraduate Bachelor degree in Engineering program in computer science or equivalent.

In addition, the following requirements must be met:

- minimum 25 credits in mathematics (equivalent to Mathematical Methods 1, 2 og 3), 5 credits in statistics and 7,5 ects i physics on a higher level is required.

Courses at the Master degree program in Computer Science.

Recommended Prerequisite: Basic course in mathematical statistics.

Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

STE6246-002 Artificial Intelligence and Intelligent Agents 5 ects

Course content

  • The students will have an introduction in time series and regressions supporting predictions based on historic data.
  • Some techniques related to image analysis and language models will be introduced and pertinent applications explored
  • Techniques like imputations, dimensionality reductions and embeddings will be introduced.
  • The course will explore in depth a selection of neural networks such as LSTM, CNN. GAN and Autoencoders.
  • The concept of learning agents in a complex, infinite and continuous action and state space will be discussed and explored.
  • The students will be introduced to advanced methods for large scale data capture with emphasis on data lakehouse with semantic data catalogues relevant for machine learning

Recommended prerequisites

DTE-3608 Artificial Intelligence and Intelligent Agents - Concepts and Algorithms, SMN6139 Discrete Mathematics II, STE6246-001 Artificial Intelligence and Intelligent Agents - Introduction

Objectives of the course

After passing this course the student should have obtained the following learning outcome:


The student shall have;

  • Deep insight in a selection of regression, clustering and vectorisation tasks for use with machine learning
  • Acquaintance with advanced neural network methods applied to time series, images and text
  • Knowledge of advanced agent-based learning, multi-agent systems (MAS)
  • Understanding of MAS-methods on a practical problem
  • Hands-on knowledge in using a data scientific methods on practical problems
  • Understanding of data collection methods for machine learning


The student should be able to;

  • To set up data scientific/machine learning projects
  • To include and use advanced software libraries for machine learning and MAS in their own program
  • To interpret machine learning results with respect to application and business impact
  • To specify a full-fledged architecture for practical, machine learning oriented data collection and management

General Competence

  • The student will be posed for industrial artificial intelligence and machine learning projects
  • Be able to to build apt agent learning systems
  • Be capable of creating smaller distributed control and optimization systems with MAS
  • Be able to enter industrial projects on Big Data

Language of instruction and examination


Teaching methods

Up to 30 hours introductory lectures.

Guided exercises

Individual project run by the students under guidance by a supervisor.

Teaching and examination Language:

Lectures will be held in English (Provided the presence of English speaking students, English will be chosen).

Lecture notes will be given in English.

Project reports must be written in the English language.

Information to incoming exchange students

This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements" section".

Master Level

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



Examination: Weighting: Duration: Grade scale:
Oral exam 1/1 45 Minutes A–E, fail F
Assignment 0/1 A–E, fail F
UiT Exams homepage

More info about the assignment

The students have to write and submit two individual semester assignments based on problem descriptions presented during the course. One assignement is directed towards the data science part of the course and will account for 65% of the grade. 35% of the grade is related to an assignment on agent engineering. The assignments need to be presented in the oral part of the examination.

More information is given by the start of the course.

More info about the oral exam

There will be an oral examination part. The student will be asked to present his or her own work based on the semester assignment produced. This could influence the evaluation of the student's work up to two grades.

Re-sit examination

There will not be arranged a re-sit exam for this course.

Info about the weighting of parts of the examination


Semester assignment 1 counts for 65% of the overall grade for the written part

Semester assignment 2 counts for 35% of the overall grade for the written part

The oral exam part will be based on the written part. This part of the exam can contribute to raising or lowering the assessment given on the written part by up to two grade points.


Semesteroppgave 1 teller 65% av samlet karakter for skriftelig del

Semesteroppgave 2 teller 35% av samlet karakter for skriftelig del

Muntlig eksamensdel vil ta utgangspunkt i den skriftelige delen. Denne eksamensdelen vil kunne bidra til å heve eller senke vurderingen gitt på skriftelig del med inntil to karaktertrinn.

  • About the course
  • Campus: Narvik |
  • ECTS: 5
  • Course code: DTE-3606
  • Tidligere år og semester for dette emnet