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Signal processing tutorial - part 2
Virtualconference
Authors : Oudre, Laurent (Author of the conference)
CIRM (Publisher )
94A12 - Signal theory (characterization, reconstruction, filtering, etc.)
CIRM (Publisher )
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Abstract :
Processing signals presents many challenges by the quantity, structure, faults, heterogeneity of sensor data recorded over time. Supporting decisions using prediction or detection based on data streams naturally calls Machine Learning techniques (and theory!) for backup. The latter field has witnessed a tremendous development since the publication of Vladimir Vapnik's best-seller 'The Nature of Statistical Learning' and the invention of Support Vector Machines, Bagging, Boosting and Random Forests between 1995 and 1999 until the latest technological breakthroughs based on Deep Learning. However, most of its reference frameworks and methods consider vector observations which are essentially invariant up to a permutation of the indices of vector components. Beyond the obvious approach of featurization (or embedding) time series into vectors of characteristics (features), there are other more subtle interactions between the two fields of SP and ML but they first need to address some fundamental questions such as:
- how to monitor the lack of stationarity in time
- dependent data - how to supervise such data
- what is the objective of learning (prediction goal) in this context, and more generally what can be learned with signals
- how to account for additional structure in signals
- how Signal Processing as a field may benefit from modern optimization techniques
The purpose of this course is to offer an overview on some Signal Processing problems from the angle of Machine Learning philosophy and techniques in order to develop insights on the fundamental questions formulated above. In other terms, this is not a standard course on Signal Processing and we may skip some of the very fundamental concepts that would belong to such a course.
The topics presented in this doctoral course will include:
- local stationarity
- event detection methodology
- prediction problems with signals
- representation learning
- graph signal processing
In the practical sessions, a concrete example in the context of precision medicine will be developed. In particular, the central issues of segmentation, quantification, representation will be addressed with code.
MSC Codes : - how to monitor the lack of stationarity in time
- dependent data - how to supervise such data
- what is the objective of learning (prediction goal) in this context, and more generally what can be learned with signals
- how to account for additional structure in signals
- how Signal Processing as a field may benefit from modern optimization techniques
The purpose of this course is to offer an overview on some Signal Processing problems from the angle of Machine Learning philosophy and techniques in order to develop insights on the fundamental questions formulated above. In other terms, this is not a standard course on Signal Processing and we may skip some of the very fundamental concepts that would belong to such a course.
The topics presented in this doctoral course will include:
- local stationarity
- event detection methodology
- prediction problems with signals
- representation learning
- graph signal processing
In the practical sessions, a concrete example in the context of precision medicine will be developed. In particular, the central issues of segmentation, quantification, representation will be addressed with code.
94A12 - Signal theory (characterization, reconstruction, filtering, etc.)
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 22/02/2021 Conference Date : 27/01/2021 Subseries : Research School arXiv category : Computer Science ; Optimization and Control Mathematical Area(s) : Computer Science ; Control Theory & Optimization Format : MP4 (.mp4) - HD Video Time : 01:08:16 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2021-01-27_Oudre_2.mp4 
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Information on the Event
Event Title : Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissageEvent Organizers : Anthoine, Sandrine ; Chaux, Caroline ; Mélot, Clothilde ; Richard, Frédéric Dates : 25/01/2021 - 29/01/2021 Event Year : 2021 Event URL : https://conferences.cirm-math.fr/2472.html
Citation Data
DOI : 10.24350/CIRM.V.19705503Cite this video as: Oudre, Laurent (2021). Signal processing tutorial - part 2. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19705503 URI : http://dx.doi.org/10.24350/CIRM.V.19705503 |
See Also
- [Multi angle] Teasing poster: mathematics, signal processing and learning / Author of the conference Antonsanti, Pierre-Louis ; Author of the conference Belotto Da Silva, André ; Author of the conference Cano, Cyril ; Author of the conference Cohen, Jeremy ; Author of the conference Doz, Cyprien ; Author of the conference Lazzaretti, Marta ; Author of the conference Pilavci, Yusuf Yigit ; Author of the conference Rodriguez, Willy ; Author of the conference Stergiopoulou, Vasiliki ; Author of the conference Kaloga, Yacouba ; Author of the conference Safaa, Al-Ali.
- [Virtualconference] Optimization - lecture 4 / Author of the conference Pustelnik, Nelly.
- [Virtualconference] Optimization - lecture 3 / Author of the conference Pustelnik, Nelly.
- [Virtualconference] Optimization - lecture 2 / Author of the conference Pustelnik, Nelly.
- [Virtualconference] Optimization - lecture 1 / Author of the conference Pustelnik, Nelly.
- [Multi angle] One signal processing view on deep learning - lecture 2 / Author of the conference Oyallon, Edouard.
- [Multi angle] One signal processing view on deep learning - lecture 1 / Author of the conference Oyallon, Edouard.
- [Virtualconference] Signal processing tutorial - part 1 / Author of the conference Oudre, Laurent.
- [Virtualconference] Reinforcement learning - lecture 4 / Author of the conference Lazaric, Allesandro.
- [Virtualconference] Reinforcement learning - lecture 3 / Author of the conference Lazaric, Allesandro.
- [Virtualconference] Reinforcement learning - lecture 2 / Author of the conference Lazaric, Allesandro.
- [Virtualconference] Reinforcement learning - lecture 1 / Author of the conference Lazaric, Allesandro.
- [Multi angle] Basics in machine learning - practical session 2 / Author of the conference Clausel, Marianne.
- [Multi angle] Basics in machine learning - practical session 1 / Author of the conference Clausel, Marianne.
- [Multi angle] Basics in machine learning - lecture 2 / Author of the conference Clausel, Marianne.
- [Multi angle] Basics in machine learning - lecture 1 / Author of the conference Clausel, Marianne.
Bibliography
- TRUONG, Charles, OUDRE, Laurent, et VAYATIS, Nicolas. Selective review of offline change point detection methods. Signal Processing, 2020, vol. 167, p. 107299. - https://doi.org/10.1016/j.sigpro.2019.107299
- LE BARS, Batiste, HUMBERT, Pierre, KALOGERATOS, Argyris, et al. Learning the piece-wise constant graph structure of a varying Ising model. In : International Conference on Machine Learning. PMLR, 2020. p. 675-684. - http://proceedings.mlr.press/v119/bars20a.html
- TRUONG, Charles, BARROIS-MÜLLER, Rémi, MOREAU, Thomas, et al. A data set for the study of human locomotion with inertial measurements units. Image Processing On Line, 2019, vol. 9, p. 381-390. - https://doi.org/10.5201/ipol.2019.265
- MOREAU, Thomas, OUDRE, Laurent, et VAYATIS, Nicolas. Dicod: Distributed convolutional coordinate descent for convolutional sparse coding. In : International Conference on Machine Learning. PMLR, 2018. p. 3626-3634. - http://proceedings.mlr.press/v80/moreau18a.html
- TRUONG, C., ruptures: a Python package for changepoint detection, 2019 - https://centre-borelli.github.io/ruptures-docs
