548-0113/01 – Methods of Experimental Data Processing (MEZEK)
| Gurantor department | Department of Geoinformatics | Credits | 5 |
| Subject guarantor | Ing. Lucie Orlíková, Ph.D. | Subject version guarantor | Ing. Lucie Orlíková, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2017/2018 | Year of cancellation | 2022/2023 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
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:
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.
Additional study materials
Way of continuous check of knowledge in the course of semester
V průběhu semestru je průběžně kontrolován postup zpracování čtyřech zadaných projektů mapování. Předmět je zakončen písemnou a ústní zkouškou.
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction