Machine Learning for Designers

Recent advances in machine learning (ML), natural language processing, image recognition, content personalization, and behavior prediction are radically changing the capabilities of software and the interfaces people use to interact with that software.

This course provides an insider's look at contemporary ML technologies and how these technologies are transforming the next generation of computing interfaces for search engines, intelligent assistants, connected homes, and open-world video games. Created for designers new to the world of machine learning, the course provides an explanation of the basic concepts of ML, offers a hands-on introduction to ML's core toolsets, and surveys the upcoming opportunities for designers with ML skills.

• Understand what machine learning and deep learning really mean; and their impacts on UX/UI design
• Learn how ML improves the ability to engage with and understand users
• Discover how machine learning systems differ from traditional computing platforms
• Learn to use Google's TensorFlow machine learning toolkit to build, train, visualize, and configure neural networks
• Explore related development, deployment and workflow management toolsets: Docker, Launchbot, and Jupyter Notebooks
• Gain hands-on ML experience by building a web app with a customized image recognition system

Patrick Hebron is a scientist-in-residence and adjunct graduate professor at NYU’s Interactive Telecommunications Program, where he leads programs focused on the intersection of art and technology. He is the founder of Foil, a digital design tool company, is author of the O'Reilly e-book, "Machine Learning for Designers," and he has worked as a software developer and design consultant for numerous corporate clients and cultural institutions.