Course details
Soft Computing
SFC Acad. year 2018/2019 Winter semester 5 credits
Soft computing covers nontraditional technologies or approaches to solving hard realworld 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).
Guarantor
Deputy Guarantor
Language of instruction
Completion
Time span
Assessment points
Department
Lecturer
Instructor
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT)
Subject specific learning outcomes and competences
 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 natureinspired 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 competences

Students will learn terminology in Softcomputing field both in Czech and in English languages.
 Students awake the importance of tolerance of imprecision and uncertainty for design of robust and lowcost intelligent machines.
Learning objectives
To give students knowledge of softcomputing theories fundamentals, i.e. of fundamentals of nontraditional technologies and approaches to solving hard realworld problems.
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.
Prerequisite kwnowledge and skills
 Programming in C++ or Java languages.
 Basic knowledge of differential calculus and probability theory.
Study literature
 Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
 Munakata, T.: Fundamentals of the New Artificial Intelligence, SpringerVerlag New York, Inc., 2008. ISBN 9781846288388
 Russel, S., Norvig, P.: Artificial Intelligence, PrenticeHall, Inc., 1995, ISBN 0133601242, second edition 2003, ISBN 0130803022, third edition 2010, ISBN 0136042597
Fundamental literature
 Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 9810247001
 Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
 Kruse, R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.: Computational Intelligence, Springer, second edition 2016, ISBN 9781447172963
 Mehrotra, K., Mohan, C., K., Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997, ISBN 0262133288
 Munakata, T.: Fundamentals of the New Artificial Intelligence, SpringerVerlag New York, Inc., 2008, ISBN 9781846288388
 Rutkowski, L.: Flexible NeuroFuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1402080425
 Russel,S., Norvig,P.: Artificial Intelligence, PrenticeHall, Inc., 1995, ISBN 0133601242, third edition 2010, ISBN 0136042597
Syllabus of lectures
 Introduction. Biological and artificial neuron, artificial neural networks.
 Acyclic and feedforward neural networks, backpropagation algorithm.
 Neural networks with RBF neurons. Competitive networks.
 Neocognitron and convolutional neural networks.
 Recurrent neural networks (Hopfield networks, Boltzmann machine).
 Recurrent neural networks (LSTM, GRU).
 Genetic algorithms.
 Optimization algorithms inspired by nature.
 Fuzzy sets and fuzzy logic.
 Probabilistic reasoning, Bayesian networks.
 Rough sets.
 Chaos.
 Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).
Syllabus  others, projects and individual work of students
Individual project  solving realworld problem (classification, optimization, association, controlling).
Progress assessment
 Midterm 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 (midterm test and project).
Course inclusion in study plans
 Programme ITMSC2, field MBI, 2nd year of study, Compulsory
 Programme ITMSC2, field MBS, MGM, MIS, MMI, MSK, any year of study, Elective
 Programme ITMSC2, field MIN, 1st year of study, Compulsory
 Programme ITMSC2, field MMM, any year of study, CompulsoryElective group N
 Programme ITMSC2, field MPV, any year of study, CompulsoryElective group B