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Updated in [June 30th, 2023]

This course provides an introduction to machine learning for musicians, artists, and other creative technologists. Students will learn the fundamentals of machine learning and how to apply them to create interactive systems for music, dance, gaming, and visual art. Through lectures, demonstrations, and hands-on exercises, students will gain experience with the machine learning pipeline, off-the-shelf tools, feature extraction techniques, and connecting machine learning to digital arts tools.

This course provides an introduction to machine learning for musicians, artists, and other creative technologists. Students will learn the fundamentals of machine learning and how to apply them to create interactive systems for music, dance, gaming, and visual art. Through lectures, demonstrations, and hands-on exercises, students will gain an understanding of the machine learning pipeline, off-the-shelf tools, feature extraction techniques, and connecting machine learning to digital arts tools. Topics of discussion include: what is machine learning, common types of machine learning, the machine learning pipeline, off-the-shelf tools, feature extraction and analysis techniques, and connecting machine learning to digital arts tools. Additionally, students will learn about cheap and easy sensing technologies that can be used as inputs to machine learning systems.

[Applications]
Upon completion of this course, students will have the knowledge and skills to apply machine learning techniques to their own projects in the arts. They will be able to create interactive systems that respond to human gesture, musical audio, and other real-time data.

[Career Path]
The recommended career path for learners of this course is Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as a deep knowledge of machine learning algorithms and techniques. They must also be able to work with large datasets and have experience with programming languages such as Python, R, and Java. Machine Learning Engineers must also be able to communicate their findings to stakeholders and be able to explain the implications of their work.

The development trend for Machine Learning Engineers is to focus on developing more efficient and accurate models, as well as exploring new applications of machine learning. As machine learning becomes more widely used, Machine Learning Engineers will need to stay up to date on the latest technologies and techniques in order to remain competitive. Additionally, Machine Learning Engineers will need to be able to work with a variety of stakeholders, from data scientists to business executives, in order to ensure that their models are meeting the needs of the organization.

[Education Path]
The recommended educational path for learners of this course is to pursue a degree in Computer Science with a focus on Machine Learning. This degree will provide students with a comprehensive understanding of the fundamentals of computer science, including algorithms, data structures, programming languages, operating systems, and computer architecture. Additionally, students will gain an in-depth knowledge of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. This degree will also provide students with the skills to develop and deploy machine learning models and applications. Finally, students will gain an understanding of the development trends in machine learning, such as natural language processing, computer vision, and robotics.