Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Modeling and optimization by learning from data (17.E2515)

Native organizations units: Sub-department for Automatic Control and Systems Engineering

Type of studies	Title
Master Academic Studies	Information Engineering (Godina: 1, Winter)
Master Academic Studies	Veštačka inteligencija i mašinsko učenje (Godina: 1, Winter)
Master Academic Studies	Information Engineering (Godina: 1, Winter)
Master Academic Studies	Biomedical Engineering (Godina: 1, Winter)
Master Academic Studies	Measurement-Information Technologies and Control Engineering (Godina: 1, Winter)
Master Academic Studies	Computing and Control Engineering (Godina: 1, Winter)
Master Academic Studies	Information and Analytics Engineering (Godina: 1, Winter)
Master Academic Studies	Veštačka inteligencija i mašinsko učenje (Godina: 1, Winter)
Master Academic Studies	Measurement-Information Technologies and Control Engineering (Godina: 1, Winter)
Master Academic Studies	Information and Analytics Engineering (Godina: 1, Winter)

General information:

Category	Scientific-professional
Scientific or art field	Automatic Control and System Engineering
Interdisciplinary	No
ECTS	6

Educational goal:

Students learn about systems of automatic control based on computer intelligence methods.

Educational outcome:

The acquired knowledge can be used in solving concrete engineering problems.

Course content:

Application of artificial neural networks in the identification, diagnosis, prediction and control. Fuzzy systems in systems engineering. Neuro fuzzy systems: combining fuzzy logic and neural networks in control. Genetic algorithms in systems engineering. Design of classic and neuro fuzzy regulators using genetic algorithms. Support vector machines and their application in identification and control of systems.

Teaching methods:

Lectures. Computational and computer practice. Consultations. The exam is written and oral. Passing the written part is the prerequisite for the oral part. The final grade is formed on the bases of achievements at the colloquium, homework assignments and the quality of the written and oral part of the exam.

Literature:

Authors	Title	Year	Publisher	Language
R.L.Haupt; S.E.Haupt	Practical Genetic Algorithms	2004	Wiley-Interscience	English
V.Kecman	Learning and Soft Computing	2001	MIT Press	English
Mykel J. Kochenderfer & Tim A. Wheeler	Algorithms for Optimization	2019	MIT Press	English
Željko Kanović, Zoran Jeličić & Milan Rapaić	Evolutivni optimizacioni algoritmi u inženjerskoj praksi	2017	FTN Izdavaštvo, Novi Sad	Serbian language