Teaching Inference for large-scale time series with application to sensor fusion (english)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 fusion by Bayesian (e.g. Kalman) filtering or for studying changes in natural/biological phenomena.Estimation methods (french)The students treat observations affected by uncertainty in a rigorous manner. They master the main methods to adjust measurements and to estimate parameters. They apply specific models to real-world problems encountered in various experimental sciences.Sensing and spatial modeling for earth observation (english)The course is organized in three main parts. 1. 3D reconstruction from images Processes of image creation Image matching, orientation and camera calibration Construction of digital elevation models (DEM) and orthophotos 2. Environmental monitoring with machine learning Extracting features from elevation or image data PredSensor orientation (english) Robotics practicals (english)The goal of this lab series is to practice the various theoretical frameworks acquired in the courses on a variety of robots, ranging from industrial robots to autonomous mobile robots, to robotic devices, all the way to interactive robots.
