Computational Intelligence (FSI-9VIN)

Academic year 2021/2022
Supervisor: prof. Ing. Radomil Matoušek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech or English
Aims of the course unit:
To give students knowledge of Computational Intelligence fundamentals, i.e. of fundamentals of nature-inspired approaches to solving hard real-world problems. Namely of fundamentals for solving of optimization problems, mathematical models and classification. The various evolutionary algorithms, optimization metaheuristics and artificial neural networks will be presented.
Learning outcomes and competences:
Understanding of basic methods of Computational Intelligence and ability of their implementation.
Prerequisites:
The knowledge of basic relations of the optimization and statistics.
Course contents:
Computational Intelligence covers a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modeling can be useless. The course introduces basic approaches and advance methods used in the field. Practical use of the methods is demonstrated on solving simple engineering problems. Students will be given time to practice of own optimization tasks.
Teaching methods and criteria:
The course is taught through lectures and individual consultations explaining the basic principles and theory of the discipline.
Assesment methods and criteria linked to learning outcomes:
Submitting and defence the project which present/uses implementation of selected CI method.
Controlled participation in lessons:
The attendance at lectures is recommended. Education runs according to individual schedules. The form of compensation of missed seminars is fully in the competence of the tutor.
Type of course unit:
    Lecture  10 × 2 hrs. optionally                  
Course curriculum:
    Lecture The lectures are divided into four blocks:
Block 1: Relationship between Computational Intelligence and Artificial Intelligence. Presentation of engineering tasks. Presentation of student tasks.
Block 2: Evolutionary algorithms, optimisation metaheuristics, swarm intelligence (Genetics Algorithms, Grammatical Evolution, Genetic Programming, Ant Colony Optimisation, metaheuristics HC12).
Block 3: Artificial Neural Networks (feedforward neural networks, recurrent neural networks, self-organisation, deep learning)
Block 4: Individual consultations for own tasks.
Literature - fundamental:
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. Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
D-APM-P full-time study --- -- DrEx 0 Recommended course 3 1 W
D-APM-K combined study --- -- DrEx 0 Recommended course 3 1 W