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 imple
The course deals with the concept of measuring in different domains, particularly in the electrical, optical, and microscale domains. The course will end with a perspective on quantum measurements, wh
A first course in statistical time series analysis and applications.
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, w
The course covers the fundaments of bioelectronics and integrated microelectronics for biomedical and implantable systems. Issues and trade-offs at the circuit and systems levels of invasive microelec
The course will cover the fundamentals of lasers and focus on selected practical applications using lasers in engineering. The course is divided approximately as 1/3 theory and 2/3 covering selected
Determination of spatial orientation (i.e. position, velocity, attitude) via integration of inertial sensors with satellite positioning. Prerequisite for many applications related to remote sensing, e
Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinki
This lecture provides insights in the design and technologies of Internet-of-Things sensor nodes, with focus on low power technologies. The lectures alternate every two weeks between sensing technolog
Large-scale time series analysis is performed by a new statistical tool that is superior to other estimators of complex state-space models. The identified stochastic dependences can be used for sensor
