Robust and Biomedical Data Science
The lecture begins by covering basic concepts of robust statistics and data science. Then we will move on to supervised and unsupervised learning problems and describe current robust data science methods (e.g., in regression and classification, cluster analysis), before treating the challenging high-dimensional setting, where structural assumptions, such as sparsity are needed to find a solution.
Biomedical applications including health monitoring using wearable devices, and genomic data analysis are discussed. Open-source toolboxes will be provided, enabling students to try out the methods that are discussed in the lecture. All materials including video lectures, scripts and additional reading material, toolboxes, and our book on robust statistics are available in the moodle course.
Please register via TUCaN to gain access to the moodle course.