Courses
BSc Programme
Taught in Danish.
Bacheloruddannelsen i sundhed og informatik
Kurset er en introduktion til programmering i Python med fokus på databehandling og -analyse. Kurset indeholder grundlæggende begreber fra programmering såsom datatyper, kontrolstrukturer, løkker, funktioner, objektorienteret programmering, pattern matching (regulære udtryk) og beregningskompleksitet. Yderligere opnås der også grundlæggende tekniske færdigheder, der kan bruges i en datavidenskabssammenhæng; herunder brug af eksterne servere og navigation på samme vha. en Unix/Linux kommandolinje.
MSc Programme
The course introduces students to the many advanced techniques researchers use to capture biological structure including various X-ray and neutron techniques, single molecule fluorescence methods, cryo-electron microscopy, nuclear magnetic resonance spectroscopy and hydrogen deuterium exchange mass spectrometry. This will involve hands on experimental exercises during which students learn how to use these methods in order to derive the three-dimensional structures of proteins. To become properly familiar with the different techniques, students will visit and conduct exercises at the large-scale infrastructure facilities in the region, such as ESS and MAX-IV in Lund, as well as the state-of-the-art facilities at UCPH such as the Core Facility for Integrated Microscopy, the cOpenNMR facility for nuclear magnetic resonance spectroscopy and the facility for biological small-angle X-ray scattering, CPHSAXS. Building on this, the course will provide an overview of how computational methods in structural biology, e.g. molecular dynamics simulations and Monte Carlo methods, can be used to study the structure and dynamics of proteins. The course will also teach students to integrate data obtained from various structural biology techniques using state-of-the-art computational modelling and machine learning methods. This will involve hands-on experience in computational experiments.
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
MSc Programme in Computer Science
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.
Numerical optimisation is a useful computer tool in many disciplines like image processing, computer vision, machine learning, bioinformatics, eScience, scientific computing and computational physics, computer animation and many more. A wide range of problems can be solved using numerical optimisation like; inverse kinematics in robotics, image segmentation and registration in medical imaging, protein folding in computational biology, stock portfolio optimisation, motion planning and many more.
In the classical machine learning data are collected and analysed offline and it is assumed that new data come from the same distribution as the data that the algorithm was trained on. If not, all the theoretical guarantees become void and the empirical performance may deteriorate dramatically. But what if we want to design an algorithm for playing chess? The opponent is not going to sample the moves from a fixed distribution.
Uncertainty is a central concept in many areas of Science and Society, yet it is often neglected in Machine Learning. This course demonstrates how the probabilistic framework gives us a powerful language to describe uncertainties about both models and predictions. We will cover a range of different probabilistic modelling techniques, and demonstrate the impact of uncertainty quantification on real-world data. Finally, we will demonstrate how model design and inference can be cleanly separated using modern probabilistic programming languages, making it possible to express complex models in a modular and concise form.