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Lecture Robust Data Science With Biomedical Applications

Masters Lecture Robust Data Science With Biomedical Applications Begins October 18th

2023/10/09 by

The lecture covers fundamental topics and recent developments in robust data science. Unlike classical statistical learning and signal processing, which relies strongly on the normal (Gaussian) distribution, robust methods can tolerate impulsive noise, outliers and artifacts that are frequently encountered in biomedical applications.

Robust data science and biomedical application lectures alternate. Exercises revise the theory and apply robust machine learning and signal processing algorithms to real world data. Software toolboxes in Python, Matlab and R that implement the lecture contents are available to the students.

Methods include:

  • Basics on robust statistical learning
  • Robust regression models
  • Robust clustering and classification
  • Robust time-series and spectral analysis
  • High-dimensional robust data science

Applications include:

  • Body-worn and radar-based sensing of vital signs
  • Electrocardiogram (ECG) and Photoplethysmogram (PPG)
  • Biomarker selection
  • Eye research
  • Genomics
  • Intracranial Pressure (ICP)