TDT4171 - Artificial Intelligence Methods


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Passed/Failed

Evaluation Weighting Duration Grade deviation Examination aids
Arbeider 20/100
Home exam 80/100 4 hours

Course content

This course is a continuation of TDT4136 Introduction to Artificial Intelligence. The three main ways of reasoning (rule-based, model–based, and case-based), will be discussed, with most focus given to model-based reasoning. In particular, we work with reasoning based with uncertain and/or partly missing information, as well as the basis for learning systems (machine learning). The reasoning frameworks that are most prominent in the course are Bayesian networks and decision graphs, but an introduction to neural networks is also included.

Learning outcome

Knowledge: The candidate will get knowledge of: - General principles for artificial intelligence (AI) - Efficient representation of uncertain knowledge - Decision making principles - Learning/adaptive systems. Skills: - Assess different frameworks for AI in given contexts - Build systems that realises aspects of intelligent behaviour in computer systems. General competence: - Know AI's basis taken from mathematics, logic and cognitive sciences.

Learning methods and activities

Lectures, self study and exercises.

Further on evaluation

The final grade is decided by the final written exam (80%) and exercises (20%). 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

Stuart Russel, Peter Norvig: Artificial Intelligence. A Modern Approach, Third Edition, Prentice Hall, 2010. Any additional material will be distributed through the course's webpage.

Credit reductions

Course code Reduction From To
IT2702 3.7 AUTUMN 2007
IT272 3.7 AUTUMN 2007
MNFIT272 3.7 AUTUMN 2007
TDT4170 3.7 AUTUMN 2007
SIF8031 3.7 AUTUMN 2007
IT3704 3.7 AUTUMN 2008
MNFIT374 3.7 AUTUMN 2008
MNFIT374 3.7 AUTUMN 2008
More on the course



Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III


Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English, Norwegian

Location: Trondheim

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

Department with academic responsibility
Department of Computer Science


Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Arbeider 20/100
Room Building Number of candidates
Spring ORD Home exam (1) 80/100





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.
  • 1) Merk at eksamensform og karakterregel er endret som et smittevernstiltak i den pågående koronasituasjonen.

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

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