PV069 Hybrid Systems of Machine Learning

Faculty of Informatics
Spring 2004
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
Guaranteed by
prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. Ing. Jan Žižka, CSc.
Timetable
Wed 16:00–17:50 B011 and each odd Wednesday 18:00–19:50 B116, each odd Wednesday 18:00–19:50 B117
Prerequisites (in Czech)
! P069 Hybrid Systems of Machine Learning
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This subject introduces basic principles of artificial neural networks and their applications from the practical point of view. It focuses on utilization of combined (hybrid) machine learning methods both for applications and automated support of designing learning algorithms by selected machine learning methods.
Syllabus
  • Artificial perceptrons and neural networks. Basic learning algorithms, delta-rule, back propagation of errors. Features of basic neural network models, problems with over-training and network designs.
  • Transformation of decision trees to neural networks, initialization of weigths.
  • Genetic algorithms, their combinations with neural networks, optimalization ow weigths.
  • Hybrid neural networks, combination of inputs and weights using t-norms and t-conorms. AND and OR fuzzy neuron. Fuzzy-neural networks. ANFIS and NEFCON architectures. Neuro-fuzzy classifiers. Optimalization of fuzzy sets within IF-THEN rules.
  • Recurrent, Hopfield, and avalanche networks. Other types of networks.
  • Application examples.
Literature
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
Assessment methods (in Czech)
Výuka je formou přednášek a cvičení (cvičení jsou jednou za 2 týdny 2 hod. a studenti si v nich vyzkoušejí některé z probíraných metod pomocí existujících softwarových nástrojů). Zkouška je písemná.
Language of instruction
Czech
Further Comments
Study Materials
The course is also listed under the following terms Spring 2003, Spring 2006, Spring 2007.
  • Enrolment Statistics (Spring 2004, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2004/PV069