Syllabus academic year 2011/2012
(Created 2011-09-01.)
APPLIED ARTIFICIAL INTELLIGENCEEDA132
Credits: 7,5. Grading scale: TH. Cycle: G2 (First Cycle). Main field: Technology. Language of instruction: The course might be given in English. EDA132 overlaps following cours/es: EDA131. Optional for: D4, D4pv, F4, Pi4. Course coordinator: Jacek Malec, Jacek,Malec@cs.lth.se, Computer Science. Prerequisites: EDAA01 Programming-Second Course or EDA027 Algorithms and Data Structures. Assessment: Written examination. The final grade of the course is based on the result of the written examination and the quality of the compulsory assignments. Parts: 2. Further information: Detailed rules concerning the assignments will be found in the course program. Home page: http://www.cs.lth.se/eda132.

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.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

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

Literature
Russell, S & Norvig, P: Artificial Intelligence - A Modern Approach, 3rd Ed. Prentice Hall 2010. ISBN: 0-13-207148-7

Parts

Code: 0104. Name: Written Examination.
Higher education credits: 3. Grading scale: TH. Assessment: Written examination. Contents: Written examination of the material covered by the course.

Code: 0204. Name: Assignments.
Higher education credits: 4,5. Grading scale: UG. Assessment: The assignments are evaluated and graded. To pass the course all the compulsory assignments must be approved. Details regarding the compulsory assignments will be found in the course program (syllabus) at the course web site. Contents: Assignments related to some of the treated subdomains are implemented to give practical experience of difficulties such as computational complexity, scalability and result interpretation.