This course introduces students to the many concepts and techniques in Artificial Intelligence (AI). Topics covered include: problem spaces and search, heuristic search techniques, predicate logic, game playing techniques, planning, learning, natural language processing, and machine perception

Course Objectives

1. Analysing cognitive models

2.Analysing interaction models

3.Develop sample smart systems

Recommended or Required Reading

Luger, George; Stubblefield, William; 2004; “Artificial Intelligence: Structures and Strategies for Complex Problem Solving”; The Benjamin/Cummings Publishing; ISBN 0805347801 ,Russell, Stuart; Norvig, Peter; 2003; “Artificial Intelligence – A Modern Approach”; Prentice-Hall; 0131038052 ,Giarratano, Joseph C.; Ritel, Gary D.; 2005; “Expert Systems – Principles and Programming”; Thompson Course Technology; 0534384471 ,Munakata, Toshinori; 2007; “Fundamentals of the New Artificial Intelligence Neural, Evolutionary, Fuzzy and More”; Springer; 9781846288395 ,Maedche, Alexander; 2002 ; “Ontology learning for the semantic web”; Kluwer Academic Publishing; 0792376560 ,Hjelm, Johan; 2001; “Creating the semantic Web with RDF: professional developer s guide”; John Wiley; ,Goertzel, Ben; Pennachin, Cassio; 2007; “Artificial General Intelligence”; Springer; 9783540237334 ,Nabiyev, Vasif V.; 2005; “Yapay Zeka”; Seçkin Yayıncılık; 9753479859

Learning Outcomes

1. Effectively use evolutionary computation in applications

2. Effectively use artificial neural networks in applications

3. Effectively use fuzzy logic in applications

4. By using these meta heuristics, provide applications with smart behaviours

Grading

Midterm: 24%

Quiz: 6%

Homework: 10%

Research Presentation: 30%

Final: 30%