Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

Patient Specific 3D Surfaces for Interactive Medical Planning and Training

The 3D Surface representation (S-rep) can be employed to illustrate solid models of physical objects. 3D S-reps have been successfully used in CAD/CAM, and in conjunction with texture mapping, in the modern gaming industry to customize avatars, improve the gaming realism and sense of presence. Current healthcare systems require patient specific information sharing for planning, training and patient education. We are proposing a cost effective method to generate patient specific S-reps and convert them to optimized X3D models in the context of medical simulations for planning, training and patient education. We assess the accuracy of the S-rep and its potential for inclusion in Web3D-based interactive medical simulations. We exemplify with an interactive X3D tool for medical planning and training of the complex X-Ray and Proton therapy procedures