Data Science Education: The Signal Processing Perspective

Gannot S, Tan ZH, Haardt M, Chen NF, Wai HT, Tashev I, Kellermann W, Dauwels J (2023)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2023


Book Volume: 40

Pages Range: 89-93

Journal Issue: 7


DOI: 10.1109/MSP.2023.3294709


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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Gannot, S., Tan, Z.-H., Haardt, M., Chen, N.F., Wai, H.-T., Tashev, I.,... Dauwels, J. (2023). Data Science Education: The Signal Processing Perspective. IEEE Signal Processing Magazine, 40(7), 89-93.


Gannot, S., et al. "Data Science Education: The Signal Processing Perspective." IEEE Signal Processing Magazine 40.7 (2023): 89-93.

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