COURSE DESCRIPTION
Advanced Data Processing Methods in Geosciences
Programme:
Environmental and Regional Studies (3rd level)
Modul:4D Earth
Course code: DIZ01
Study year: None
Course principal:
Asst. Prof. Gorazd Žibret, PhD
ECTS: 6
Workload: lectures 10 hours, seminar 10 hours, tutorial 10 hours, individual work 150 hours
Course type: general elective
Languages: Slovene, English
Learning and teaching methods: lectures, discussion classes, seminars, independent work assignments, consultations, e-learning.
Prerequisits:
Second-cycle Bologna degree in the relevant track or a university (level VII) degree
Content (Syllabus outline):
- Introduction to artificial intelligence
- Data in geosciences and its specifics:
- compositional and
- orientational data,
- outliers,
- attributive data
- Neural networks:
- properties,
- types,
- topologies,
- feed-forward and
- recurrent networks
- Neural network machine learning paradigms:
- supervised,
- unsupervised,
- reinforced,
- evolutionary
- Data preparation and validation methods
- Positive and negative aspects of neural networks
- Approach to the problem
- Examples from practice
- Other methods of artificial intelligence:
- linear and multiple regression,
- decision trees,
- support vector machines,
- fuzzy logic
- individual work
Readings:
- Nielsen A.M. Neural Networks and deep learning. Determination press, 2015, 224 p.
- https://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf
- http://neuralnetworksanddeeplearning.com/
- Haykin S. Neural Networks and learning machines, 3rd ed. Prentice Hall, 2009. https://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf
- ŽIBRET, Gorazd, ŠAJN, Robert. Hunting for geochemical associations of elements: factor analysis and self-organising maps. Mathematical geology. 2010, vol. 42, no. 6, str. 681-703. DOI: 10.1007/s11004-010-9288-3.
- ŽIBRET, Gorazd, ŠAJN, Robert, ALIJAGIĆ, Jasminka, STAFILOV, Trajče. Use of neural networks in the geochemical data interpretation. Zeitschrift für geologische Wissenschaften. 2012, bd. 40, h. 4/5, str. 253-266.
- CERAR, Sonja, MEZGA, Kim, ŽIBRET, Gorazd, URBANC, Janko, KOMAC, Marko. Comparison of prediction methods for oxygen-18 isotope composition in shallow groundwater. Science of the total environment. 2018, vol. 631-632, str. 358-368. DOI: 10.1016/j.scitotenv.2018.03.033.
Objectives and competences:
Student knows principles of artificial intelligence, and can use neural networks for solving problems
Intended learning outcomes:
The student is able to independently apply neural network methods using the MemBrain simulator. He/she knows how to analyse a problem, determine suitable network topologies, prepare data, train the network, properly evaluate the resulting model and use it in practice.
Learning and teaching methods:
- Lectures
- Seminar
- Independent work assignments
- Consultations
- e-Learning