Details for the Course Syllabus for Course EDAF70F valid from Spring 2017

General

Aim

To give an introduction to several subdomains of artificial intelligence and to give an orientation about fundamental methods within these domains. To convey knowledge about breath and depth of the domain.

Contents

Intelligent agents. Heuristic search. Game programming. Knowledge based systems. Machine learning. Natural language. Semantic Web. Autonomous robots. Planning.

Knowledge and Understanding

For a passing grade the doctoral student must
display basic knowledge concerning theories and methods related to the following subdomains: intelligent agents, heuristic search, game programming, knowledge representation, knowledge-based systems, probabilistic reasoning, machine learning, natural language processing.

Competences and Skills

For a passing grade the doctoral student must
complete a number of assignments based on problems related to some of the following subdomains: heuristic search, knowledge-based systems, probabilistic reasoning, machine learning, natural language processing.

Judgement and Approach

For a passing grade the doctoral student must
demonstrate the ability to critically evaluate and compare different methods used in artificial intelligence.

Types of Instruction

Examination Formats

Admission Requirements

Assumed Prior Knowledge

Selection Criteria

Literature

Literature:
Stuart Russell, P.: Artificial Intelligence , A Modern Approach. Pearson, 2010. ISBN 0132071487.

Further Information

Course code

Administrative Information

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