Courses – University of Copenhagen

Courses

BSc Programme

Virksomhedsprojekt (Project in Practice)

MSc Programme

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

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

Linux and Python Programming

This course introduces you to: a) Unix/Linux: basic navigation, pipes, configuring the shell, standard unix tools, networking, process control. b) Programming: programming basics, data types, conditionals, loops, functions, object oriented programming, pattern matching (regular expressions), computational complexity. 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

Natural Language Processing (NLP)

Have you ever wondered how to build a system that can process, understand or generate text automatically? For instance, to translate between languages, answer questions, or recognise the names of people in text? Then this course is for you. More information

Project in Practice

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

Ekstra information / Sidebar