Course Name
Artificial Intelligence and Logical Programming
Course Overview 
Level of study	Master Degree 	
Workload	ECTS: 		3
Total Hours:	108
Contact Hours:	48
•	Lectures:	16
•	Labs:		16
•	Seminars:	16	
Course Code	М2.В.2.1	
Semester	Fall	
Prerequisites	Modern issues of informatics and computer science, Intelligent
systems, Decision making theory	
Course Objectives	The main goal - to prepare students for research and design activity.	
Learning Outcomes	To study the methods of computing intelligence; the
methods of its application to solution of practical problems;
to develop software implementation of neural
networks, evolutionary algorithms and fuzzy systems.
By the end of the course the students will know:
modern techniques and approaches of computing intelligence,
methods of its application to solution of practical
problems of artificial cognition and management.
The students will be able to:
set the problem and develop algorithms for its solution
using methods of computing intelligence, carry out the
analysis of research literature, carry out the analysis of
received solutions.
The students will have the experience in:
application technologies of methods of computing intelligence
for solving practical problems, skills in development
and testing of artificial neural networks, evolutionary
algorithms and fuzzy systems in one of the highlevel
programming languages.	
Syllabus	Topic 1 Artificial neural networks (lectures 2)
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Topic 2 Evolutionary algorithms (lectures 2, labs 4)
Topic 3 Fuzzy logic (lectures 4, labs 4)
Topic 4 Hybrid techniques (lectures 4, labs 4)
Topic 5 Application of computing intelligence methods
(lectures 4, labs 4)
A	
Labs	Lab 1 Classification tasks 
Lab 2 Approximation tasks 
Lab 3 Hybrid models 
Lab 4 Project (Team Work)	
Assessment	Credit test	
Resources	Compulsory
1. S. Osovsky. 2002. Neural networks for information
processing. – М.: Finansy i statistika, 344p.
2. Luke S. Essentials of Metaheuristics. A Set of Undergraduate
Lecture Notes. September, 2009. Text:
http://cs.gmu.edu/~sean/book/metaheuristics/. Russian
translation:http://qai.narod.ru/GA/metaheuristics.html.
3. V. G. Spitsyn, Y. R. Tsoy. 2007. Knowledge representation
in information systems: handbook. – Tomsk:
TPU publishing, 160p.
4. I. Z. Batyrshin, A. A. Nedosekin, A. A. Stetsko, V. B.
Tarasov, A. V. Yazenin, N. G. Yarushkina. 2006. Theory
and practice of fuzzy hybrid systems /Edited by N.G.
Yarushkina. – М.: Phyzmatlit.
Further reading
1. S. Khaikin. 2006. Neural networks: full course. – М.:
Williams, 995p.
2. V. G. Redjko. 2003. Evolutionary cybernetics. М. –
Nauka, 156p. (Informatics: infinite possibilities and possible
limits).
3. V. G. Redjko. 2005. Evolution, neural networks, intelligence.
Models and concepts of evolutionary cybernetics.
Moscow: URSS.
Software and Internet resources
1. Neural Computation Journal.
http://www.mitpressjournals.org/loi/neco.
2. Evolutionary Computation Journal.
http://www.mitpressjournals.org/loi/evco.
3. EJ «Neuroinformatics».
http://www.niisi.ru/iont/ni/Journal/.
4. EJ «Fuzzy systems and soft computing».
http://fuzzy.tversu.ru/.
5. Neural Networks Research Group.
http://nn.cs.utexas.edu/
6. Evolutionary Complexity Research Group (EPlex).
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http://eplex.cs.ucf.edu/
7. Russian association of neuroinformatics.
http://ni.iont.ru/
8. Russian association of artificial intelligence.
http://www.raai.org/
9.Russian association of fuzzy systems and soft computing.
http://www.ransmv.narod.ru/	
Instructors	Axyonov Sergey Vladimirovich
Assistant Professor
Optimization System Control Department
Institute of Cybernetics
http://portal.tpu.ru/SHARED/a/axoenowsw/eng