Computational Geosciences
The computational geosciences program within the Department of Geosciences offers workshops and courses on mathematical and numerical methods, computational tools, modeling and numerical simulation to Bachelor and Master students, doctoral candidates and postdocs of the entire Department.
Supervision of theses and dissertations in various fields of Computational Geosciences is also offered. For more information, please visit the webpage of the Computational Geosciences working group.
Machine Learning in the Climate Sciences
Workshop Description: The widespread use of machine learning (data-driven artificial intelligence algorithms) is rapidly reshaping the research landscape both in industry and academia. This two-day workshop is aimed at climate scientists and meteorologists interested in a fast-track introduction to machine learning techniques and their applications. It consists of brief hands-on lectures on key concepts such as supervised and unsupervised machine learning, overfitting in high-dimensional problems, regression and classification, pattern recognition and dimension reduction. These lectures will be complemented by interactive computer-based exercises in Python. The workshop concludes with a hackathon where participants can apply all new concepts in practice.
Language: The lectures will be delivered in English; individual advice on the computer exercises can be given both in English and German.
Prerequisites: Basic programming knowledge (not necessarily in Python) is a prerequisite. In general, participants should be able to read and modify pre-written code with additional support by the lecturer.
Time: 10.09.2019 - 11.09.2019
Location: PC-Pool (Course room 3) of the RRZK, University of Cologne
Speaker: Dr. Peer Nowack, Imperial College London
Organizers: Dr. Eric Parteli, Dr. Mark Reyers
Funding information: This workshop is funded by grants from the German Research Foundation awarded to Dr. Eric Parteli and Dr. Mark Reyers within the Competence Area 3 of the University of Cologne
Registration: to be announced
Website: https://complexsystems.uni-koeln.de/mlclimate2019.html
Modelling Human Dispersal Using Agent-Based Modelling
Speakers: Dr. Iza Romanovska, Barcelona Supercomputing Center; Dr. Fulco Scherjon (The Netherlands Organisation for Applied Scientific Research)
Time: 30.09.2019 - 01.10.2019
Location: Archaeological Institute, University of Cologne
Organizers: Prof. Eleftheria Paliou, Dr. Eric Parteli
Funding information: This workshop is funded by grants from the German Research Foundation awarded to Prof. Eleftheria Paliou and Dr. Eric Parteli within the Competence Area 3 of the University of Cologne
Website: http://archaeologie.uni-koeln.de/abmworkshop2019.html
Numerische Simulation der Atmosphäre
Dr. Eric Parteli contributes a practical lab ('Praktikum') for the BSc lecture Numerische Simulation der Atmosphäre at the Institute of Geophysics and Meteorology at UoC.
Language: German
Location / time: Please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
Computational Geosciences Tutorial
Description: The tutorial provides a platform for students from the entire Department to discuss mathematical and numerical methods in Computational Geosciences that are relevant for their research projects. The discussions will take place in small groups and may be complemented with short courses offered by the lecturer in Computational Geosciences.
Pre-requisites: The student should be involved in at least one research activity within the Department, and the aim of using the Computational Geosciences method(s) should be to solve a specific problem in the scope of the student's research activity(ies). Please note that in this tutorial, the themes of the discussion rounds and the courses will vary according to the computational methods and the research topics. Therefore, please send an email to eric.parteli
uni-koeln.de with a short description of your computational geosciences problem *in advance* for appropriately scheduling your participation.
Literature: Lecture notes as well as textbooks depending on the computational method
Location / time: Institute of Geophysics and Meteorology, room 3.159 / Wed, 14:00 - 18:00
Language: German, English
Course instructor: Dr. Eric Parteli
To register: see above
Error Analysis in Geosciences
Description: Any measurement, however careful and scientific, is subject to some uncertainties. This course aims at providing the students with knowledge of mathematical techniques to study and evaluate these uncertainties. In particular, the main functions of the error analysis are to estimate how large the uncertainties in a given measurement or calculation are and to evaluate what can be done to reduce these uncertainties, when necessary. In the course, particular focus shall be given to error propagation and tests of statistical significance of experimental data. The course will cover enough derivation to justify and understand the equations, but will emphasize methods and examples more than theory. Examples in lectures and exercises shall focus on geoscientific applications. The students shall acquire the ability to successfully apply error analysis in their research activities and scientific works in the Department of Geosciences. Detailed list of contents is available in KLIPS 2.0.
Pre-requisite: Knowledge of partial differentiation and multiple integration, and knowledge of programming in MATLAB (minimum is knowledge of how to program for loops, as well as operations with vectors and matrices).
Literature: Bevington, P.R., Keith Robinson, D., Data Reduction and Error Analysis for the Physical Sciences, McGrawHill, 3rd edition (2002); Taylor, J.R., An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books, 2nd edition (1997), among others.
Language: English
Location / time: Please check course availability and schedule in KLIPS 2.0.
Course instructor: Dr. Eric Parteli
To register:
Registration for students of the Master "Physics of the Earth and Atmosphere" via KLIPS 2.0
GSGS students can register via email to eric.parteliuni-koeln.de.
R for statistics and data analysis in Geosciences
Description: This course offers an introduction to the programming environment R and its applications for data analysis and statistics. The course consists of lectures and practical labs (hands-on sessions), and will address the following topics:
- getting started in R
- data manipulation, loops and conditional execution
- descriptive statistics
- regression and correlation analysis
- multivariate statistical methods
- significance tests
- graphics and visualization of large/multivariate data sets
The lectures will cover a brief theoretical introduction that is essential to understand the different statistical methods, but emphasis in the course will be given to implementation/application of these methods with R, examples from different geoscientific areas, and interpretation. Interested participants will be offered a project on correspondence analysis and seriation for geochronology.
Pre-requisites: All members of the Graduate School of Geosciences (GSGS), Cologne, who are interested in using R for statistics and data analysis can participate. However, some experience with basic statistics is important to follow the course. This experience may have been gained, for instance, from a bachelor/master course that involved basic aspects of statistics.
Language: English
Location:
Institute for Geophysics and Meteorology
Pohligstr. 3, 3rd floor
room 3.134 (CIP)
Schedule:
- Please check course availability in KLIPS
Course instructor: Dr. Eric Parteli
To register: Please register your interest in this course by sending an e-mail to gsgs-admin@uni-koeln.de with your name and the course code (2017_T12R). Please note that the maximum number of participants is 20.
See also: http://www.geosciences.uni-koeln.de/training0.html
Diskrete-Elemente-Simulation
This course consists of an introduction to numerical simulations by means of the Discrete-Element-Method (DEM), practical exercises and project. The course includes training in High Performance Computing to perform the DEM simulations.
Pre-requisite: The participant should have knowledge of at least one programming language.
Literature: Lecture notes and scientific publications related to the project topic
Language: German / English
Location / time: please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
To register: email to eric.parteliuni-koeln.de
Morphodynamic Modeling
This course consists of an introduction to morphodynamic modeling, practical exercises and project. The course includes training in High Performance Computing to perform the numerical simulations.
Pre-requisite: The participant should have knowledge of at least one programming language.
Literature: Lecture notes and scientific publications related to the project topic
Language: German / English
Location / time: please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
To register: email to eric.parteliuni-koeln.de
Quantitative Methods in Environmental Archaeology
Dr. Eric Parteli contributes a lecture on Quantitative Methods in Environmental Archaeology for the Master Dialog in Environmental Archaeology.
Language: English
Location / time: Please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
Einführung in die Multivariate Statistik
KLIPS 2.0: Fachgruppe Geowissenschaften / VO / LV-Nr: 14892.0230
Pre-requisite: Lecture "Statistik und mathematische Verfahren" (Dannenberg, Institute of Geography)
Literature: Bahrenberg, G., Giese, E., Mevenkamp, N., Nipper, J., Statistische Methoden in der Geographie, Band 2: Multivariate Statistik, Borntraeger, 3., neubearbeitete Auflage (2008)
Language: German
Location / time: Please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
Data Mining & Practical Machine Learning
Description: The course shall present to participants, by means of tutorials and hands-on sessions, tools and techniques to capture and store vast quantities of data; find patterns, trends and anomalies in large datasets; summarize data with simple quantitative models; automatically extract models from data, and validate the extracted models.
Contents (not exhaustive): Structural patterns, Machine Learning and statistics, introduction to concepts, instances and attributes, knowledge representation, decision trees, algorithms (Bayesian models, association rules, instance-based learning, linear models and clustering), evaluation, implementations and transformations. A JAVA-based Graphical-User-Interface software is being planned for the practical labs.
Pre-requisites: Basic knowledge of statistics is required, and you will profit the most from the course if you bring experience with data analysis from your research. The course is primarily intended for Master students and/or Doctoral candidates.
Schedule: Please check course availability and schedule in KLIPS 2.0
Course instructor: Dr. Eric Parteli
