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

Abstract

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.

Teaching Machine Learning in School: A Systematic Mapping of the State of the Art

“…The IU designed by ReadyAI ( 2019) is based on the 5 big ideas as being proposed by the AI4K12 guidelines. Sakulkueakulsuk et al, 2018) based the IU on the "Four P's of Creative Learning" framework developed by MIT Media Lab and the IU designed by Zimmermann-Niefield et al (2019a) is based on Interactive Machine Learning (Fiebrink, 2019). None of the encountered IUs provides more complete information on the methodology used for its development.…”

From rituals to magic: Interactive art and HCI of the past, present, and future

“…Unlike many other domains, creators may be more interested in the aesthetic quality or utility of a machine learning model than in faithfully modeling a pre-existing training set, making "interactive" machine learning approaches in which people manipulate training data to steer model performance appropriate for many tasks (Fiebrink, Cook, & Trueman, 2011). Yet, even with algorithms and software packages tailored to creative work, creators may still struggle with understanding how to best configure machine learning algorithms to achieve the desired results, or even understanding what machine learning algorithms are capable of (Fiebrink, 2019). The creative use of machine learning also raises tricky new ethical and legal questions.…”

Designing Deep Reinforcement Learning for Human Parameter Exploration

“…Careful interaction design must be considered to balance full automation with full user control and aim at creating flow states among people [21]. Aiming at such user engagement may also constitute a design opportunity to demystify AI systems, notably by having users learn from experience how algorithms work with data [32].…”