Overview

The 4-volume set CCIS 2558 – 2561 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, which took place in Vilnius, Lithuania, in September 2024.  The 132 full papers included in these proceedings were carefully reviewed and selected from a total of 272 submissions to the workshops. The papers were organized topical sections as follows: Part I: Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM Workshop 2024), Machine Learning for Sustainable Power Systems (ML4SPS), Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD) and Workshop on Bias and Fairness in AI (BIAS 2024). Part II: Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIM[1]LAI), 2nd Workshop on Hybrid Human-Machine Learning and Decision Making HLDM’24, 15th International Workshop on Machine Learning and Music (MML 2024), Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2024). Part III: 21st International Workshop on Mining and Learning with Graphs (MLG 2024), 2nd International Workshop on Deep Learning meets Neuromorphic Hard[1]ware (website), International Workshop on Machine Learning for Irregular Time Series (ML4ITS 2024), Workshop on Machine Learning for Earth Observation (MACLEAN 2024) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2024) and International Workshop and Tutorial on Data-Centric Artificial Intelligence (DEARING). Part IV: Workshop on Data Science for Social Good (SoGood 2024), 7th Workshop on Machine Learning for CyberSecurity (MLCS 2024), Tutorial and Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI), Workshop on MIning DAta for financial applicationS (MIDAS), Workshop on Advancements in Federated Learning (WAFL), Workshop on Mining and Learning Real-world Dynamics via High-order Networks (MLH).

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