The IMAGE Section offers courses in image analysis and processing, computer vision, computer simulation, numerical optimization, information retrieval, and machine learning. We suggest two study tracks within the MSc Computer Science study programme for specializing in Data Science or Image Analysis and Computer Vision.

Currently, the following MSc courses are offered/planned, starting with the academic year 2018/2019:

Advanced Topics in Image Analysis (ATIA)

The purpose of this course is to expose the student to selected advanced topics in image analysis. The course will bring the student up to a level sufficient for master thesis work within image analysis and computer vision. Focus is not on specific topics, but rather on recent research trends. More information

Advanced Topics in Machine Learning (ATML)

The purpose of this course is to expose students to selected advanced topics in machine learning. The course will bring the students up to a level sufficient for writing a master thesis in machine learning. More information

Applied Programming (APP)

The purpose of the course is to introduce the programming language C/C++, key programming concepts in a scientific context, and guidelines for documentation. The course will enable the student to develop the C/C++ code needed to process large amounts of scientific data that cannot be handled in interpreted languages such as MATLAB, Python, Maple, or R. The teaching will be based on examples from linear algebra. More information

Computational Methods in Simulation (CMiS)

The aim of this course is to create an overview of typically used simulation methods and techniques. The course seek to give insight into the application of methods and techniques on examples such as motion of deformable models, fluid flows, heat diffusion etc. During the course the student will be presented with mathematical models such as a system of partial differential equations. The course seek to teach the student the classical approaches to reformulate and approximate mathematical models in such a way that they can be used for computations on a computer. More information

Computer Game Development Project (DADIU)

The purpose of this course is to teach the art and science of creating computer games. This course is given in collaboration with the Danish National Academy of Digital Interactive Entertainment (DADIU). Students from different universities and art schools are taught together in 3 phases: Joint curriculum, Game Workshop and Graduation Game. Computer Science students are given the role as game programmer, possibly lead programmer. The course is particularly relevant for students wishing to work with computer game creation after their studies.More information

Information Retrieval (IR)

The course objective is to offer an advanced introduction into information retrieval. The goal is to understand and model how people search for, access and use information, in order to design and evaluate reliable retrieval algorithms. Through realistic and sound projects, the course aims to stimulate and prepare students for their MSc thesis work. More information

Large-Scale Data Analysis (LSDA)

In comparison to other courses dealing with machine learning or data analysis, the focus of this course is on the peculiarities of processing large amounts of data - that is, on Big Data. More information

Machine Learning (ML)

The purpose of the course is to introduce students to the basic theory and most common techniques of statistical machine learning. The students will obtain a working knowledge in statistical machine learning. More information

Medical Image Analysis (MIA)

This course will give an introduction to medical image formation in the different scanning modalities: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of detection, registration, and segmentation, and end with specific applications in clinical practise. A key to achieve success in the medical image analysis is formal evaluation of methodologies, thus a introduction to performance characterisation will also be a central topic. More information

Numerical Optimization (NO)

This course will build up a toolbox of numerical optimization methods which the student can use when building solutions in his or her future studies. Therefore this course is an ideal supplement for students coming from many different fields of science. This course teaches the basic theory of numerical optimization methods. The focus is on deep learning and of how the methods covered during the course works. Both on a theoretical level that goes into deriving the math but also on an implementational level with focus on computer science and good programming practice. More information

Signal and Image Processing (SIP)

The course introduces basic computational, statistical, and mathematical techniques for representing, modeling, and analysing signals and images. Signals and images are measurements, which change with time and/or space, and these measurements typically originate from a physical system ordered on a grid. Examples are 1-dimensional sound, 2-dimensional images from a consumer camera, 3-dimensional reconstructions from medical scanners, and movies. More information

Vision and Image Processing (VIP)

Vision and Image Processing (VIP) gives a overview of modern vision techniques used in man and machine. Focus is both on conceptual understanding of the models and methods and on practical experience. The course covers state of the art methods for image analysis including how to solve visual processing tasks such as object recognition and content based image search and retrieval. More information

Web Science (WS)

The course objective is to offer an advanced introduction into Web Science. The goal is to understand and model the Web as a structure and to design and evaluate some of the major technologies operating on the Web (see below). Through realistic and sound projects, the course aims to stimulate and prepare students for their MSc thesis work. More information