autumn 2023
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 stp

Course content

  • The students will have an introduction in time series and regressions supporting predictions based on historic data. ARMA techniques will be addressed.
  • Preprocessing techniques like PCA will be practiced.
  • Practical use of cluster techniques, Support Vector Machines and Classification and Regression trees (CART) will be exercised
  • The course will explore in depth a selection of neural networks such as LSTM and CNN.
  • The students will be introduced to MAS systems and theoretical concepts associated with these i.e. ontologies, ACL, architectures, game theory and MAS-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 neural network methods
  • Deep insight in CART and ensemble methods
  • Hands-on knowledge in using a data scientific method on a practical problem
  • Knowledge of agent-based learning and multi-agent systems (MAS)
  • Hands-on knowledge in using MAS-methods on a practical problem


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 set up distributed control systems using MAS

General Competence

  • The student will be posed for industrial artificial intelligence and machine learning projects
  • 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

15 hours introductory lectures.

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 report 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: Grade scale:
Assignment 1/2 A–E, fail F
Assignment 1/2 A–E, fail F
UiT Exams homepage

More info about the assignment

The students have to submit two individual semester assignments. Students have to use software at UiT in Narvik.

More information is given by the start of the course.

Re-sit examination

There will not be arranged a re-sit exam for this course.
  • About the course
  • Campus: Narvik |
  • ECTS: 5
  • Course code: DTE-3606
  • Tidligere år og semester for dette emnet