548-0113/01 – Methods of Experimental Data Processing (MEZEK)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorIng. Lucie Orlíková, Ph.D.Subject version guarantorIng. Lucie Orlíková, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year1Semestersummer
Study languageCzech
Year of introduction2017/2018Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
JUR02 Ing. Lucie Orlíková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

Knowledge • Basics of machine learning • Basics of statistics and geostatistics • Basics of working with data in R • To present basics of data driven modelling • To present basics of data driven modelling • To understand and to use artificial neural networks of different architectures for environmental data analysis • To present basics of data driven modelling • To present fundamental ideas of statistical learning theory

Teaching methods

Lectures
Tutorials

Summary

The main goal of this course is to introduce to students the basics of artificial neural networks. This course will cover basic neural network architectures and learning algorithms, for applications for environmental data analysis and modelling. The students will have a chance to try out several of these models on practical problems

Compulsory literature:

HAYKIN, Simon S. Neural networks and learning machines. 3rd ed. Upper Saddle River: Pearson, 2009. 934 s. ISBN 9780131293762. KOHONEN, Teuvo. Self-Organizing Maps. Berlin: Springer-Verlag, 1995. 392 s. Springer Series in Information Sciences 30. ISBN 3-540-58600-8. info Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. EPFL Press, 2009. Bishop C. Pattern recognition and machine learning. Springer, 2006.

Recommended literature:

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. 2d edition. Springer, 2009. Kanevski M. (Editor). Advanced Mapping of Environmental Data. Geostatistics, Machine Learning, and Bayesian Maximum Entropy. iSTE/Wiley, 2008.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

Další požadavky na studenta nejsou stanoveny.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1)Introduction to artificial neural networks 2) Architecture of artificial neural networks. Perceptrons and basic learning algorithms 3) Backpropagation learning 4) Competitive Learning and Kohonen Nets 5) CounterPropagation method 6) Hopfield Nets and Boltzmann Machines 7) Optimization Techniques, overfitting, cross validation 8) Support vector classification 9) Support vector machine - kernel methods 10) Artificial neural networks in geoinformatics

Conditions for subject completion

Full-time form (validity from: 2017/2018 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67  18
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner