@article{gannot2023data,
author = {Gannot, Sharon and Tan, Zheng-Hua and Haardt, Martin and Chen, Nancy F. and Wai, Hoi-To and Tashev, Ivan and Kellermann, Walter and Dauwels, Justin},
title = {Data Science Education: The Signal Processing Perspective [SP Education]},
year = {2023},
month = {November},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/data-science-education-the-signal-processing-perspective-sp-education/},
pages = {89-93},
journal = {IEEE Signal Processing Magazine},
volume = {40},
number = {7},
}