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autumn 2018

STE6246-002 Artificial Intelligence and Intelligent Agents - 5 ECTS

Sist endret: 29.01.2019

The course is provided by

Faculty of Engineering Science and Technology


Narvik |

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.

Type of course

The course may be taken as a single subject.

Admission requirements

A relevant undergraduate bachelor Engineering programme with minimum 30 credits mathematic/statistics topics

Application Code: 9371

Required prerequisite(s) knowledge:

  • STE6246-001 Knowledge based systems, theoretical part
  • SMN6139 Discrete mathematics

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

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:

Knowledge 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

Skills 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


Teaching methods

15 hours introductory lectures. Individual project run by the students under guidance by a supervisor.


Course work requirement: Minimum requirements for reporting structure and written format prior to submission. Students have to use software at the UiT in Narvik.

Examination and assessment: Project report (100%). Letter grading A - F, where F is fail grade. There will not be arranged a re-sit exam for this course.  

Date for examination

Report - Artifical Intelligence and Intelligent Agents hand in date 06.11.2018

The date for the exam can be changed. The final date will be announced in the StudentWeb early in May and early in November.


Recommended reading/syllabus

The syllabus (project description) will be presented in the beginning of the course.