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

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

Bar-Ilan University

Research output: Contribution to journal › Article › peer-review

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
State	Published - 1 Nov 2023

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2023.3294709

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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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doi = "10.1109/MSP.2023.3294709",

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AU - Wai, Hoi To

AU - Tashev, Ivan

AU - Kellermann, Walter

AU - Dauwels, Justin

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N2 - 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.

AB - 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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JO - IEEE Signal Processing Magazine

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ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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