Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2017-03-22
Division: Computer Science (LTH)
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: EDA132
Teaching language: Swedish
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.
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.
Intelligent agents. Heuristic search. Game programming. Knowledge based systems. Machine learning. Natural language. Semantic Web. Autonomous robots. Planning.
Stuart Russell, P.: Artificial Intelligence , A Modern Approach. Pearson, 2010. ISBN 0132071487.
Types of instruction: Lectures, laboratory exercises
Examination format: Written exam
Grading scale: Failed, pass
Assumed prior knowledge: EDAA01 Programming - Second Course
Web page: http://cs.lth.se/eda132-applied-artificial-intelligence/