| Course Unit Code | 548-0113/01 |
|---|
| Number of ECTS Credits Allocated | 5 ECTS credits |
|---|
| Type of Course Unit * | Choice-compulsory |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | First Year |
|---|
| Semester when the Course Unit is delivered | Summer Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | Czech |
|---|
| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
|---|
| Name of Lecturer(s) | Personal ID | Name |
|---|
| JUR02 | Ing. Lucie Orlíková, Ph.D. |
| 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 |
| Learning Outcomes of the Course Unit |
|---|
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
|
| Course Contents |
|---|
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 |
| Recommended or Required Reading |
|---|
| Required Reading: |
|---|
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. |
ŠÍMA, Jiří a Roman NERUDA. Teoretické otázky neuronových sítí. Vyd. 1. Praha: Matfyzpress, 1996. 390 s. ISBN 80-85863-18-9.
|
| Recommended Reading: |
|---|
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. |
M. Meloun, J. Militký: Statistické zpracování experimentálních dat, Academia Praha 2004.
V. Mařík, O. Štěpánková, J. Lažanský a kol.: Umělá inteligence, Academia Praha 2003. |
| Planned learning activities and teaching methods |
|---|
| Lectures, Tutorials |
| Assesment methods and criteria |
|---|
| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
|---|
| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 33 | 17 |
| Examination | Examination | 67 | 18 |