University of Oslo

This course offers an introduction to self-adapting methods also known as artificial intelligence or machine learning. Students study schemes for classification, search and optimization based on bio-inspired mechanisms, evolutionary computation, and artificial neural networks. This includes specialized approaches such as swarm intelligence and artificial immune systems. The course also provides an overview of alternative traditional methods.

This course covers the principles for evolution of DNA and gene products as well as the use of genetic data in evolutionary studies of organisms. It starts with a theoretical introduction to important evolutionary processes in the eukaryotic genome and genome components. Teachings also include molecular techniques, bioinformatics, and evolutionary bio-statistics. Students acquire knowledge of the different molecular processes that lead to changes in the genome as well as the evolutionary consequences the different processes have. Students become familiar with the various population genetic processes such as genetic drift, non-random mating, and different forms of selection, while understanding the connection between molecular processes in the genome and evolutionary processes in populations and species. Through this course, students become familiar with internet resources on bioinformatics, statistical analysis of molecular data, and interpretations of the results. They also learn important molecular laboratory methods and gain experience with relevant laboratory work.

This course covers the practical and theoretical principles of remote sensing used in Earth observation. Students understand the content of data produced in these processes and how to extract specific information. Students learn to determine which techniques are best used in different situations.

This course gives insight into theory and algorithms for fundamental mathematical problems associated with systems of equations, optimization, and approximation of functions. There are many applications, for example, the representation of geometry, images, and more generally large data sets. Applications in various areas of the natural sciences are an essential part of the course. This subject assumes familiarity with linear algebra and calculus of several variables. The course discusses topics including important numerical methods; strategies both for constructing methods and analyzing them, often with the help of linear algebra; the limitations of various mathematical approximations and the effect of computer rounding errors; concrete methods for solving various kinds of equation systems and non-linear optimization problems; and methods for approximating functions with the help of simpler functions, such as polynomials, piecewise polynomials, and trigonometric functions.