Examination arrangement

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.

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