Bi7445 Artificial neural networks for biologists

Faculty of Science
Spring 2007
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
Mgr. Tomáš Hudík (seminar tutor)
Guaranteed by
doc. Ing. Jan Žižka, CSc.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Artificial perceptrons, linear and nonlinear units, neural networks. Basic algorithms of learning, the delta rule, gradient searching, error backpropagation. Radial basic functions and RBF networks. Properties of basic neural-network models, the problem of over-training and the network design, types of solved tasks. Kohononen maps as clustering. Fuzzy neurons, fuzzy neural networks. Recurrent networks, Hopfield networks, avalanche networks. Examples of applications. Computer experiments with artificial and real data in exercises using the Statistica and other systems.
Literature
  • Mitchell, T. (1997) Machine Learning. McGraw-Hill.
  • Baldi, P. and Brunak, S. (2001) Bioinformatics: The machine learning approach. Second Edition. The MIT Press.
  • Duda, R. O., Hart, P. E., and Stork, D. G. (2001) Pattern Classification. Second Edition. John Wiley & Sons.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2008.
  • Enrolment Statistics (Spring 2007, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2007/Bi7445