548-0113/02 – 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 languageEnglish
Year of introduction2017/2018Year of cancellation2022/2023
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

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

During the semester, the progress of the four assigned mapping projects is continuously checked. The course ends with a written and oral examination.

E-learning

Other requirements

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, validity until: 2022/2023 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67  18 3
Mandatory attendence participation: Lectures optional, exercises 80%

Show history

Conditions for subject completion and attendance at the exercises within ISP: Lectures by self-study of course materials available at http://geoscience.vsb.cz/. Possibility of personal or on-line consultation. Participation in exercises according to the student's possibilities. To obtain credit, the student must complete a credit project assigned by the instructor no later than the end of the examination period of the semester. The exam must be taken in person.

Show history

Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan
2021/2022 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan
2020/2021 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 1 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

Předmět neobsahuje žádné hodnocení.