ECE 188: Machine Learning for the Arts - Spring 2019


Thursday 3/21/2019, 10am - 3pm


Course Description

This course explores the vital new domain of Machine Learning (ML) for the arts. Though born out of computer science research, contemporary ML techniques are reimagined through creative application to diverse tasks such as style transfer, generative portraiture, music synthesis, and textual chatbots and agents. Through direct, hands-on experience with state of the art ML tools, students will develop their skills in this nascent area and form critical perspectives on the strengths and limitations of current approaches.

As ML permeates multiple aspects of culture, industry, and scholarship, it is essential both to train the next generation of ML-literate artists and engineers, and to equip them with critical tools to evaluate these new techniques. How do computational tools augment, complicate, or supercede human creative endeavor? What new approaches to artistic production are possible with the advent of affordable graphics hardware and ML software?

This project-based course will be conducted primarily in python using free, open-source machine learning and scientific computing toolkits, running on cloud-based educational computing resources. In addition to hands-on experience with ML techniques, students will become familiar with cloud-based workflows, jupyter notebooks, and kubernetes containers. Architectures and topics covered include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), LSTMs, Wavenets, Generative Adversarial Networks (GANs) and others. Students will be responsible both for technical implementation and creative value of course projects.

Prequisites: ECE16, ECE143, or equivalent course on Python. Students need to attend the bootcamp 3/21 to enroll in the course.



Introduction to Art and ML (Week 1)

Text Generation (Week 2-3)

Time Series in ML (Week 4)

Visual Processing (Week 5)

Advanced Generative Systems (Week 6-8)

Final Project Development (Week 9-10)

Final Presentations / Exhibition (Finals Week)