Course - Artificial Intelligence Methods - TDT4171 - Studies
TDT4171 - Artificial Intelligence Methods
About
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
This course is a continuation of TDT4136 Introduction to Artificial Intelligence. The three main ways of reasoning (rule-based, modelbased, 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.
Recommended previous knowledge
TDT4136 Introduction to Artificial Intelligence, or equivalent.
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 | 01.09.2007 | |
| IT272 | 3.7 | 01.09.2007 | |
| MNFIT272 | 3.7 | 01.09.2007 | |
| TDT4170 | 3.7 | 01.09.2007 | |
| SIF8031 | 3.7 | 01.09.2007 | |
| IT3704 | 3.7 | 01.09.2008 | |
| MNFIT374 | 3.7 | 01.09.2008 | |
| MNFIT374 | 3.7 | 01.09.2008 |
No
Version: 1
Credits:
7.5 SP
Study level: Third-year courses, level III
Term no.: 1
Teaching semester: SPRING 2020
No.of lecture hours: 2
Lab hours: 3
No.of specialization hours: 7
Language of instruction: Norwegian
Location: Trondheim
- Computer Systems
- Informatics
Department with academic responsibility
Department of Computer Science
Phone:
Examination
Examination arrangement: Portfolio assessment
- Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
- Spring ORD work 20/100
-
Room Building Number of candidates - Spring ORD Written examination 80/100 D
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"