English

Not applicable.

Student gets acquirement of theoretical and practice knowledge of pattern recognition, artificial neural networks, expert systems application of automation.

The subject knowledge on the secondary school level is required.

Not applicable.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.

Automation - relevance, resources, engineering cybernetics.
Data, information, knowledge - definition, examples.
Expert systems - definitiv, architectural engeneering, theoretical sources, characteristics, inference engine, creation of knowledge base, acguirement of kbnowledges, proces sof consultation, aplications.
Artificial neural networks - definition, neuron, topology, paradigm, multilayer perceptrons neural network, backpropagation algorithm, activation, characteristics.
Machine learning - definitions, preprocessing, supervised learning, unsupervised learning, meta-learning, optimisation algorithms.
Theory of Inventive Problem Solving - analysis of the object to be improved and task to be solved, solving of inventive tasks supported by expert system and information from patent databases.
Computer vision - introduction, digital image processing, image preprocessing, image segmentation.

Not applicable.

The aim of the course is to acquaint the students with modern methods and means in automation. To get knowledge and experience of pattern recognition and using neural networks, expert systems in automation.

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Not applicable.

Not applicable.

Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (EN)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (EN)

Berka P. a kol.: Expertní systémy. Skripta, VŠE Praha, 1998. (CS)
Hlaváč V.- Šonka M.: Počítačové vidění. Grada 1992,Praha,ISBN 80-85424-67-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 1. ACADEMIA 1993,Praha,ISBN 80-200-0496-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 2. ACADEMIA 1997,Praha,ISBN 80-200-0504-8 (CS)