OpenMined has released a course to train next-generation machine learning enthusiasts and practitioners to process sensitive data without breaching privacy. OpenMined is well known as a community focussed on developing tools and frameworks for AI that can work with data that can not be pooled centrally for privacy concerns. This course is a part of their collaboration with PyTorch to offer four free courses (The Privacy AI Series) on machine learning with privacy-preserving techniques.
Currently, there are four courses planned to be offered — Our Privacy Opportunity, Foundations of Private Computation, Federated Learning Across Enterprises, and Federated Learning on Mobile.
At present, OpenMined has released the first course “Our Privacy Opportunity. The course is being offered free of cost along with a completion certificate. The best part is that you will be working on real-world projects while being mentored by world-class researchers with names ranging from Andrew Trask, PhD Researcher at the University of Oxford, Cynthia Dwork, author of Differential Privacy, Harvard, Ilya Mironov, author of Renyi Differential Privacy, FAIR, and more.
The course is aimed at dealing with current privacy infrastructures, their limitations, and building the foundations for upcoming courses on federated learning. As per the course design, it will take you around the privacy-transparency tradeoff and teach you about the principles of privacy. Moreover, the first course requires you to only invest a little over seven hours. At the end of the course, you will be able to come up with privacy product specifications on your own.
The course has been structured for beginners and hence, assumes no prerequisites. It begins by defining information flow, then puts lights on failures in terms of privacy and transparency in the information structure. After exposing the lacunas of current information flow designs, the course builds upon structured transparency and its impact.
Register for the first course of The Private AI Series here.
