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Music information retrieval (MIR) is an exciting and challenging research area that aims to develop techniques and tools for organizing, analyzing, retrieving, and presenting music-related data. At the intersection of engineering, social sciences, and humanities, MIR relates to different research disciplines, including signal processing, machine learning, information retrieval, psychology, musicology, and the digital humanities. In Dagstuhl Seminar 24302, we explored advancing technology and education in these fields by examining learning from various angles, using music as a concrete application domain. Typically, learning in computer science brings to mind data-driven techniques like deep learning. While machine learning was crucial to the seminar, we aimed to go beyond a technical perspective, focusing on educational and pedagogical aspects. Specifically, we investigated how music can serve as a vehicle to make learning in signal processing and machine learning interactive and effectively communicated in interdisciplinary research and educational settings. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary describing the main topics, goals, and group activities. Then, we give an overview of the participants' stimulus talks and subsequent discussions (listed alphabetically by the main contributor’s last name) and summarize further activities, including group discussions and music sessions.