Magnetic resonance imaging (MRI) and spectroscopy (MRS) will be addressed in detail, along with experimental design, data gathering and processing on MRS, structural and functional MRI in humans and rodents, and hands-on experience with MRI scanners. ...
The course establishes the concepts of statistical analysis, probability theory, and data analysis. This includes standard statistical tests, correlation analysis and experimental design. It introduces computational statistical methods to analyse large dat ...
This advanced theoretical course introduces students to basic concepts in wave scattering theory, with a focus on scattering matrix theory and its applications, in particular in photonics. ...
This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce student ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...
The learning outcome is to increase the knowledge of simulation methods and the role of computers in the management and the operation of electric power systems. ...
The course introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies with a focus on the biophysical level. ...