Data Science Education: The Signal Processing Perspective [SP Education]

Sharon Gannot; Zheng Hua Tan; Martin Haardt; Nancy F. Chen; Hoi To Wai; Ivan Tashev; Walter Kellermann; Justin Dauwels

doi:10.1109/MSP.2023.3294709

Data Science Education: The Signal Processing Perspective [SP Education]

Sharon Gannot, Zheng Hua Tan, Martin Haardt, Nancy F. Chen, Hoi To Wai, Ivan Tashev, Walter Kellermann, Justin Dauwels

Signal Processing Systems

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Abstract

In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML) - more specifically, deep learning - methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.

Original language	English
Pages (from-to)	89-93
Number of pages	5
Journal	IEEE Signal Processing Magazine
Volume	40
Issue number	7
DOIs	https://doi.org/10.1109/MSP.2023.3294709
Publication status	Published - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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title = "Data Science Education: The Signal Processing Perspective [SP Education]",

abstract = "In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML) - more specifically, deep learning - methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.",

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AU - Tashev, Ivan

AU - Kellermann, Walter

AU - Dauwels, Justin

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Data Science Education: The Signal Processing Perspective [SP Education]

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