TDT4173 - Machine Learning


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
work 20/100
Written examination 80/100 4 hours D

Course content

The course gives an introduction to the principles and methods for automatic learning in computer systems. Classical syntax-based learning methods as well as more knowledge-intensive methods are described. Main empahsis is on symbolic methods, where explicit concepts and relationships are learned. Statistical generalizations, ensemble methods, and deep learning are also included. The strengths and weaknesses of various methods are discussed.
Learning methods in case-based reasoning is integrated with problem solving within the CBR cycle. Numerical and cognitive models for similarity asessment will be discussed, together with different learning system architectures. Methods that combine case-based and generalisation-based inferences will be discussed as well.

Learning outcome

The aim of the course is to introduce principles of machine learning methods in general, to give an understanding of basic mechanisms underlying various specific methods. In case-based reasoning the integration of learning and problem solving is focused.

Learning methods and activities

Lectures, colloquia, self study, exercises.

Further on evaluation

Portfolio assessment is the basis for the grade in the course. The portfolio includes a final written exam (80%) and exercises (20%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade.
If there is a re-sit examination, the examination form may be changed from written to oral.
In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken.

Course materials

Text book: Tom Mitchell: Machine learning, McGraw Hill, 1997.
Michael M. Richer and Rosina Weber: Case-Based Reasoning, Springer, 2013.
Selected papers.

Credit reductions

Course code Reduction From To
IT3704 7.5 01.09.2008
MNFIT374 7.5 01.09.2008
MNFIT374 7.5 01.09.2008
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2019

No.of lecture hours: 2
Lab hours: 3
No.of specialization hours: 7

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Industrial Economics
  • Information Security
  • Informatics
  • Psychology
  • Statistics
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science



Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD work 20/100
Room Building Number of candidates
Summer UTS work 20/100
Room Building Number of candidates
Autumn ORD Written examination 80/100 D 2019-12-13 09:00
Room Building Number of candidates
SL110 hvit sone Sluppenvegen 14 64
SL110 lilla sone Sluppenvegen 14 53
SL311 orange sone Sluppenvegen 14 65
SL274 Sluppenvegen 14 1
SL120 blå sone Sluppenvegen 14 2
Summer UTS Written examination 80/100 D
Room Building Number of candidates
  • * 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"

More on examinations at NTNU