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A pass on the course is compulsory if the student is to obtain a master’s degree in Physical Geography. The course is carried out as an independent project. In the project the student should display an ability to apply and compile…

The aim of this course is to provide students with the theoretical understanding of how a geographic database is built-up and how it can be used. You will study key concepts for handling geographic databases with special focus on spatial…

The course contains a number of different basic components of spatial analysis, both in terms of theoretical background and analytical methods. Some of the concepts and techniques covered in the course are point pattern analysis,…

The aim of this course is to give the underlying mathematical and computer science theory required in GIS. This knowledge is necessary to evaluate the results in an analysis and to carry out more advanced analyses where the tools not are…

The aim of this course is to study the use of GIS in a client-server environment, where communication takes place through the Internet. Part of the course deals with computer screen cartography. The lectures deal with the most important…

The degree project requires a literature review and special studies. In addition, it includes a number of compulsory elements, in the form of lessons and seminars, which deal with, among other things, scientific writing in English and…

The course treats: Object-oriented programming style for scientific computing. SciPy/NumPy data structures. Examples of complex numerical algorithms from different fields within numerical analysis. Coupling to numerical libraries in C…

Simulation techniques is a field which merges experience in modelling with knowledge in Scientific Computing and programming skills. The course gives students in a late stage of their university studies the possibility to work, in small…

The course is preparatory for a master's thesis in numerical analysis. The overarching goal of the course is that students acquire in-depth knowledge in a specialised area of numerical analysis and are introduced to current research in the…

The course covers: Models of computational fluid dynamics Hyperbolic conservation laws and their basic properties (weak solutions, weak entropy solutions, shocks) Discontinuous Galerkin discretizations Simulations of gas dynamics…

The course treats: Methods for time integration: Euler’s method, the trapezoidal rule. Multistep methods: Adams' methods, backward differentiation formulae. Explicit and implicit Runge-Kutta methods. Error analysis, stability and…

The course gives an introduction to error estimates, convergence and stability, as well as existence and regularity of solutions to elliptic differential equations (PDEs). This is followed by an introduction to DUNE-FEM which discusses how…

The course provides students with a general overview of the development of Greek from Antiquity to the present, focusing on the period from the late Byzantine to the modern era with regard to changes in sound system, grammar and syntax…

This course is an introduction to the rapidly growing field of Corpus Linguistics and its application in Modern Greek studies. Corpus Linguistics (CL) is a method to retrieve data to study natural occurring language in different contexts…

The ability to argue well is one of the core competencies expected of citizens, politicians, companies, and students. The purpose of this course is to discuss and provide a vocabulary and instruments for the analysis, evaluation, and…

The course introduces different theories of translation, from classical contributions to modern theories that build on semiotics, the general science of meaning. Central questions dealt with include the nature of the act of translation,…

The fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning Feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network …

In this course you will learn modern statistical computing as viewed in data science through implementations in popular computing platforms such as R and Python. It covers the following topics: The R and Python environment: R packages…