Applied Machine Learning in Data Science

Dear Colleagues,

This Special Issue aims to illuminate the diverse applications of machine learning in data science, focusing on innovative methodologies that address contemporary challenges. Central to our exploration are knowledge-informed machine learning techniques that effectively incorporate domain expertise, enhancing model performance in scenarios with limited data availability. We also emphasize data-limited machine learning approaches designed to extract meaningful insights from sparse datasets. This Special Issue will delve into multimodal learning, which integrates various data types—such as text, images, and audio—creating more comprehensive and effective models. Additionally, we encourage contributions on parameter-efficient and sample-efficient fine-tuning of pretrained large models, enabling researchers to adapt these powerful architectures to specific tasks with minimal computational resources and data. By collating research across these critical areas, this Special Issue seeks to foster collaboration and innovation, ultimately advancing the field of data science through applied machine learning techniques.

Dr. Baoquan Zhang
Prof. Dr. Yunming Ye
Prof. Dr. Xutao Li
Guest Editors

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