Course Contents
[b]Robust Data Science for Signal Processing[/b]
[list]
[*]Basics on robust statistical learning
[*]Robust regression models
[*]Robust clustering and classification
[*]Robust time-series and spectral analysis
[*]High-dimensional robust data science
[/list]
[b]Biomedical Applications[/b]
[list]
[*]Body-worn and radar-based sensing of vital signs
[*]Electrocardiogram (ECG) and Photoplethysmogram (PPG)
[*]Biomarker selection
[*]Eye research
[*]Genomics
[*]Intracranial Pressure (ICP)
[/list]
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.
Literature
A manuscript and lecture slides can be downloaded via Moodle. Further reading
[list]
[*]Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing. Cambridge University Press, 2018.
[*]Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29, No. 4, 2012, pp. 61-80.
[*]Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009.
[*]Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley Series in Probability and Statistics, 2006.
[/list]
Preconditions
Fundamental knowledge of statistical signal processing
Online Offerings
Moodle
[b]Robust Data Science for Signal Processing[/b]
[list]
[*]Basics on robust statistical learning
[*]Robust regression models
[*]Robust clustering and classification
[*]Robust time-series and spectral analysis
[*]High-dimensional robust data science
[/list]
[b]Biomedical Applications[/b]
[list]
[*]Body-worn and radar-based sensing of vital signs
[*]Electrocardiogram (ECG) and Photoplethysmogram (PPG)
[*]Biomarker selection
[*]Eye research
[*]Genomics
[*]Intracranial Pressure (ICP)
[/list]
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.
Literature
A manuscript and lecture slides can be downloaded via Moodle. Further reading
[list]
[*]Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing. Cambridge University Press, 2018.
[*]Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29, No. 4, 2012, pp. 61-80.
[*]Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009.
[*]Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley Series in Probability and Statistics, 2006.
[/list]
Preconditions
Fundamental knowledge of statistical signal processing
Online Offerings
Moodle
- Lehrende: Taulant Koka
- Lehrende: Jasin Machkour
- Lehrende: Michael Muma
- Lehrende: Fabian Scheidt
Semester: Inverno 2022/23