IMT6101 - Computational Intelligence


This course is no longer taught and is only available for examination.

Examination arrangement

Examination arrangement: Portfolio assessment and Written exam
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio assessment 50/100
School exam 50/100 3 hours

Course content

-Symbolic Learning -Statistical Learning -Artificial Neural Networks -Support Vector Machines -Cluster Analysis -Fuzzy Logic -Evolutionary Computation -Hybrid Intelligent Methods

Learning outcome

Knowledge: On concluding the course, candidates -will have an in-depth understanding of theories, methods, and algorithms in machine learning. -will be able to apply the most appropriate machine learning algorithms in various applications.

Skills: On concluding the course, candidates -will be able to evaluate and contrast basic techniques and algorithms used in machine learning. -will be able to formulate specific algorithmic requirements for a given problem and propose an appropriate solution. -will be able to predict and judge the performance of a machine learning or a data mining method.

General Competence: On concluding the course, candidates -will be able to assess the nature of a problem at hand and determine whether a machine learning technique/algorithm can solve it efficiently enough. -will strengthen their ability to work with the original scientific literature.

Learning methods and activities

Lectures Homework

Further on evaluation

Re-sit examination: For the written exam: Ordinary re-sit examination.

Course materials

Basic Textbook: Pattern Recognition and Machine Learning (Information Science and
 Statistics) by Christopher M. Bishop Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and 
David G. Stork Selected research papers Additional Literature for interested readers: 
Machine Learning and Data Mining: Introduction to Principles and Algorithms by Igor Kononenko, Matjaz Kukar 
Machine Learning by Tom M. Mitchell

More on the course



Version: 1
Credits:  5.0 SP
Study level: Doctoral degree level


Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Informatics
Contact information
Course coordinator:

Department with academic responsibility
Department of Information Security and Communication Technology


Examination arrangement: Portfolio assessment and Written exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Portfolio assessment 50/100



Room Building Number of candidates
Autumn ORD School exam 50/100 2023-12-21 09:00 PAPIR
Room Building Number of candidates
B211- 2.etg. Beryll 2
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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