Making student capable to develop and to use different Computational intelligence (sometimes called Soft computing) techniques to solve problems from various domains and to be well-prepared in practical programming.

Upon finishing this course, student will be ready for work and further specialization in the field of Computational intelligence.

• Artificial intelligence problems and solving methods. • Neural networks and their application in problem solving. • Fuzzy logic application in problem solving. • Algorithms based on Support Vector Machines. • Search and optimization problems and their solving. • Heuristic and exact methods for solving search and optimization problems. • Metaheuristics (Genetic algorithms, Simulated annealing, Electromagnetism-based metaheuristic, Tabu search, Variable neighborhood search). • Rule-based systems. • Agent-based systems. • Machine learning and techniques.

Practicing implementation and exploitation of computational intelligence methods and techniques using different data sets and tools.

None.

Total assigned hours: 90

New material: 30
Elaboration and examples (recapitulation): 0

Auditory exercises: 45
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 0
Test: 10
Final exam: 5

Activity during lectures: 0
Test/test: 0
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 30
Project: 0
Final exam: 70
Requirement for taking the exam (required number of points): 0

Vojislav Kecman: Learning and Soft Computing, MIT Press, 2001.; Konar Amit: Artificial Intelligence and Soft Computing, CRC Press, 2000; Talibi El-Gazali: Metaheuristics - from design to implementation, John Willey and Sons, 2009.