Course detail

Computational Intelligence

FSI-9VINAcad. year: 2020/2021

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.

Learning outcomes of the course unit

Understanding of basic methods of Computational Intelligence and ability of their implementation.

Prerequisites

The knowledge of basic relations of the optimization and statistics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 981-02-4700-1 (EN)
Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9 (CS)

Planned learning activities and teaching methods

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.

Language of instruction

Czech, English

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme D-APM-K Doctoral, 1. year of study, winter semester, 0 credits, recommended

  • Programme D4P-P Doctoral

    branch D-APM , 1. year of study, winter semester, 0 credits, recommended

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer

Syllabus

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.