S. Gannot et al., "Data Science Education: The Signal Processing Perspective [SP Education]," in IEEE Signal Processing Magazine, vol. 40, no. 7, pp. 89-93, Nov. 2023, doi: 10.1109/MSP.2023.3294709. keywords: {Computational modeling;Training data;Machine learning;Signal processing;Streaming media;Data science;Data models;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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Funding Info:
This is not a research study. This paper originated from a panel (Data Science Education: A Singal Processing Perspective) I participated in at IEEE ICASSP 2022 in Singapore. The other panelists expanded the discussions and invited more co-authors and turned it into a magazine article for IEEE Signal Processing Magazine, which is more on general topics instead of technical/research papers. No research funding was involved or needed.