Title:

Soft Computing

Code:SFC
Ac.Year:2018/2019
Sem:Winter
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MBI2ndCompulsory
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN1stCompulsory
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Compulsory-Elective - group N
IT-MSC-2MPV-Compulsory-Elective - group B
IT-MSC-2MSK-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/SFC/private/
Credits:5
Completion:credit+exam (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600026
 ExamsTestsExercisesLaboratoriesOther
Points:55150030
Guarantor:Zbořil František V., doc. Ing., CSc. (DITS)
Deputy guarantor:Zbořil František, doc. Ing., Ph.D. (DITS)
Lecturer:Zbořil František V., doc. Ing., CSc. (DITS)
Instructor:Zbořil František, doc. Ing., Ph.D. (DITS)
Zbořil František V., doc. Ing., CSc. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Substitute for:
Neural Networks (NEU), DITS
 
Learning objectives:
  To give students knowledge of soft-computing theories fundamentals, i.e. of fundamentals of non-traditional technologies and approaches to solving hard real-world problems.
Description:
  Soft computing covers non-traditional technologies or approaches to solving hard real-world problems. Content of course, in accordance with meaning of its name, is as follow: Tolerance of imprecision and uncertainty as the main attributes of soft computing theories. Neural networks. Fuzzy logic. Nature inspired optimization algorithms. Probabilistic reasoning. Rough sets. Chaos.  Hybrid approaches (combinations of neural networks, fuzzy logic and genetic algorithms).
Knowledge and skills required for the course:
  
  • Programming in C++ or Java languages.
  • Basic knowledge of differential calculus and probability theory.
Subject specific learning outcomes and competencies:
  
  • Students will acquaint with basic types of neural networks and with their applications.
  • Students will acquaint with fundamentals of theory of fuzzy sets and fuzzy logic including design of fuzzy controller.
  • Students will acquaint with nature-inspired optimization algorithms.
  • Students will acquaint with fundamentals of probability reasoning theory.
  • Students will acquaint with fundamentals of rouhg sets theory and with use of these sets for data mining.
  • Students will acquaint with fundamentals of chaos theory.
Generic learning outcomes and competencies:
  
  • Students will learn terminology in Soft-computing field both in Czech and in English languages.
  • Students awake the importance of tolerance of imprecision and uncertainty for design of robust and low-cost intelligent machines.
Why is the course taught:
  By studying the subject, students will gain knowledge of working with vague, uncertain and incomplete information that is essential for successful intelligent system designs.
Syllabus of lectures:
 
  1. Introduction. Biological and artificial neuron, artificial neural networks.
  2. Acyclic and feedforward neural networks, backpropagation algorithm. 
  3. Neural networks with RBF neurons. Competitive networks.
  4. Neocognitron and convolutional neural networks.
  5. Recurrent neural networks (Hopfield networks, Boltzmann machine).
  6. Recurrent neural networks (LSTM, GRU).
  7. Genetic algorithms.
  8. Optimization algorithms inspired by nature.
  9. Fuzzy sets and fuzzy logic.
  10. Probabilistic reasoning, Bayesian networks.
  11. Rough sets.
  12. Chaos.
  13. Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).
Syllabus - others, projects and individual work of students:
 Individual project - solving real-world problem (classification, optimization, association, controlling).
Fundamental literature:
 
  1. Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 981-02-4700-1
  2. Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
  3. Kruse, R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.: Computational Intelligence, Springer, second edition 2016, ISBN 978-1-4471-7296-3
  4. Mehrotra, K., Mohan, C., K., Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997, ISBN 0-262-13328-8
  5. Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008, ISBN 978-1-84628-838-8  
  6. Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
  7. Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, third edition 2010, ISBN 0-13-604259-7
Study literature:
 
  1. Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
  2. Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8
  3. Russel, S., Norvig, P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7
Progress assessment:
  
  • Mid-term written examination - 15 points.
  • Project - 30 points.
  • Final written examination - 55 points; The minimal number of points necessary for successful clasification is 25 (otherwise, no points will be assigned).
Exam prerequisites:
  At least 20 points earned during semester (mid-term test and project).
 

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