Course - Artificial Intelligence Methods - TDT4171
Artificial Intelligence Methods
About
About the course
Course content
This course is a continuation of TDT4136 Logic and Reasoning Systems. 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
In combination with TDT4136 Logic and Reasoning Systems, the subject will give a general introduction to Artificial Intelligence (AI). Its basis is taken from mathematics, logic and cognitive sciences. The subject aims at realising aspects of intelligent behaviour in computer systems.
Learning methods and activities
Lectures, self study and exercises. 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.
Recommended previous knowledge
TDT4136 Logic and Reasoning Systems, 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 |
|---|---|---|
| IT2702 | 3.7 sp | |
| IT272 | 3.7 sp | |
| IT3704 | 3.7 sp | |
| MNFIT272 | 3.7 sp | |
| MNFIT374 | 3.7 sp | |
| MNFIT374 | 3.7 sp | |
| SIF8031 | 3.7 sp | |
| TDT4170 | 3.7 sp |
Subject areas
- Computer Systems
- Informatics