BIO-614: Practical - Antanasijevic LabThe students will learn:
- how to handle viral protein antigens and antibody samples
- how to assemble and purify immune complexes using liquid chromatography
- how to image them on an electron microscope ...
CIVIL-459: Deep learning for autonomous vehiclesDeep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects, make predictions, and make decisions. (Fun fact: this s ...
COM-202: Signal processingSignal processing theory and applications: discrete and continuous time signals; Fourier analysis, DFT, DTFT,
CTFT, FFT, STFT; linear time invariant systems; filter design and adaptive filtering; sampling; interpolation
and quantization; image processing, ...
MICRO-518: Quantitative imaging for engineersThis course will arm students with knowledge of different imaging techniques for practical measurements in many different fields of engineering.
Modalities will range from drone imaging all the way down to x-ray microscopy with practical sessions ...
CS-444: Virtual realityThe goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-action background and describes the key programming techni ...
PHYS-467: Machine learning for physicistsMachine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...
ENV-548: Sensor orientationDetermination of spatial orientation (i.e. position, velocity, attitude) via integration of inertial sensors with satellite positioning. Prerequisite for applications related to remote sensing, environmental monitoring, mobile mapping, robotics, space expl ...
EE-607: Advanced Methods for Model IdentificationThis course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels. ...
ME-422: Multivariable controlThis course covers methods for the analysis and control of systems with multiple inputs and outputs, which are ubiquitous in modern technology and industry. Special emphasis will be placed on discrete-time systems due to their relevance to digital and embe ...
BIO-639: Practical - D'Angelo LabCells produce thousands of lipids that impact biological processes in ways we are only starting to characterize. In this course we will introduce MALDI imaging mass spectrometry to study lipid composition in a space resolved fashion. ...
BIO-603(SH): Practical - Stahlberg LabCryo-electron microscopy structural analysis of proteins. The course aims at demonstrating the workflow from sample purification to determining the atomic structure of a soluble or membrane protein. ...
AR-413: Constructing the view: still lifeThis course explores visual strategies and techniques for creating apparent reality. The course concentrates on the field of 3D computer graphics and the production of still lifes as computer-generated images (CGI). ...
CH-457: AI for chemistryThe AI for Chemistry course will focus on teaching students how to use machine learning algorithms and techniques to analyze and make predictions about chemical data. The course will cover topics such as the basics of machine learning, common algorithms an ...
CS-413: Computational photographyThe students will gain the theoretical knowledge in computational photography, which allows recording and processing a richer visual experience than traditional digital imaging. They will also execute practical group projects to develop their own computati ...
