Machine Learning is a foundational discipline that forms the basis of much modern data analysis. It combines theory from areas as diverse as Statistics, Mathematics, Engineering, and Information Technology with many practical and relevant real life applications. The focus of the current summer school is big data analytics, distributed inference, scalable algorithms, and applications to the digital economy. The event is targeted at research students, IT professionals, and academics from all over the world.
This school is suitable for all levels, both for researchers without previous knowledge in Machine Learning, and those wishing to broaden their expertise in this area. That said, some background will prove useful. For a research student, the summer school provides a unique, high-quality, and intensive period of study. It is ideally suited for students currently pursuing, or intending to pursue, research in Machine Learning or related fields.
Faculty Organizers: Zico Kolter, Alex Smola