TDT4171 - Artificial Intelligence Methods


Examination arrangement

Examination arrangement: School exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 100/100 4 hours D

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.

Compulsory assignments

  • Mandatory assignments

Further on evaluation

The final grade is fully determined by the exam. A number of assignments are given out during the semester. A number of these must be passed to be eligible for exam. Details will be given at the start of the course.

If the course is still not passed after a postponed exam, or by repeating the course, the approved assignments from earlier semester may be approved by the department.

The written exam will be given in English only.

If there is a re-sit examination, the examination form may change from written to oral.

Course materials

Stuart Russel, Peter Norvig: Artificial Intelligence. A Modern Approach, Fourth Edition, Pearson, 2020. 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 2024

Language of instruction: English

Location: Trondheim

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

Department with academic responsibility
Department of Computer Science


Examination arrangement: School exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 100/100 D 2024-05-30 09:00 INSPERA
Room Building Number of candidates
SL111 grønn sone Sluppenvegen 14 50
SL111 brun sone Sluppenvegen 14 81
SL111 lyseblå sone Sluppenvegen 14 72
SL111 blå sone Sluppenvegen 14 36
C218 Ankeret/Hovedbygget 1
M438 Eksamensrom 4.etg, Inngang D Mustad, Inngang D 1
SL110 lilla sone Sluppenvegen 14 41
Summer UTS School exam 100/100 D INSPERA
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