About the Instructor
John Keefe was most recently the investigations editor at Quartz. There he has designed and created the AI Studio, a "teach-by-example" effort to help journalists at Quartz and other news organizations use machine learning in their reporting. He also teaches classes on bots, machine learning, and product prototyping at the Craig Newmark Graduate School of Journalism at CUNY.
Before joining Quartz, Keefe was Senior Editor for Data News at public radio station WNYC, leading a team of journalists who specialize in data reporting, coding, and design for visualizations and investigations. He was previously WNYC's news director for nearly a decade.
A self-described "professional beginner" Keefe is the author of Family Projects for Smart Objects: Tabletop Projects That Respond to Your World from Maker Media, which grew from his effort to make something new every week for a year. Keefe has led classes and workshops at Columbia University, Stanford University, the New School University, and New York University.
He also has served as an Innovator in Residence at West Virginia University's Reed College of Media. Keefe blogs at johnkeefe.net and tweets as @jkeefe.
Recognize cases when machine learning can help in investigations, use tools to tackle real-world reporting issues, and better understand how AI is used by your sources.
Sifting through terabytes of documents or images might take years — unless you teach a computer to do it for you. Like a bloodhound, a machine-learning algorithm can take a "sniff" or sample, of what you're looking for and find "more like this."In this class, students will learn to recognize cases when machine learning might help solve such reporting problems, to use existing and custom-made tools to tackle real-world issues, and better understand AI principles and pitfalls in general.
Take this class if you are a data journalist or anyone looking to learn more about the practical journalistic applications of artificial intelligence.
General principles behind machine learning, explained in ways tailored for journalists
Coding "notebooks" and open-source machine learning libraries
Hands-on experience using machine learning to classify images.
A hands-on introduction to using machine learning for text documents.
Real-world examples of how machine learning has helped journalists, including some unexpected examples of how image-detection can be helpful.
This webinar was recorded on May 5 and 12, 2020. Closed Captioning available for this video.
Level: Beginner to Intermediate