About DLRL Summer School
About the Event
In 2005, CIFAR’s Learning in Machines & Brains program hosted its first Deep Learning Reinforcement Learning summer school in Toronto with the goal of fostering the next generation of AI researchers. Many of the former students are now leaders at some of the top tech firms and university labs.
Today, the DLRL Summer School is a part of both the CIFAR Learning in Machines & Brains program and CIFAR Pan-Canadian AI Strategy’s National Program of Activities, and is delivered in partnership with Canada’s three national AI Institutes, Amii, Mila and the Vector Institute.
This year’s DLRL Summer School happens July 27 to August 4, 2020 in Montreal, Quebec, Canada. The event brings together graduate students, post-docs and professionals to cover the foundational research, new developments, and real-world applications of deep learning and reinforcement learning. Participants learn directly from world-renowned researchers and lecturers.
Related extracurricular activities will include an AI Career Fair, industry and partner-sponsored events, as well as tourism events.
Deep Learning Summer School (DLSS)
Deep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language understanding, robotics, game playing, graphics, health care, and genomics. The DLSS will cover both the foundations and applications of deep neural networks, from fundamental concepts to leading-edge research results.
Reinforcement Learning Summer School (RLSS)
Reinforcement Learning is a family of approaches for developing systems that learn optimal behaviour through interaction with an environment. In recent years, reinforcement learning has seen success as an essential component of Deep Reinforcement Learning, which has helped AI researchers achieve previously unheard of results in games like Go and in the development of autonomous vehicles. The RLSS will cover the basics of reinforcement learning and show its most recent research trends and discoveries, as well as present an opportunity to interact with graduate students and senior researchers in the field.
Who Should Apply?
The DLRL Summer School is aimed at graduate students, postdocs, and industry professionals who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning and reinforcement learning) and wish to learn more about this rapidly growing field of research. Participants should have advanced prior training in computer science and mathematics.
How are Applications Selected?
DLRLSS will use a scoring system to select the majority of participants. Decisions will be made stochastically, and applicants with a higher score have a higher probability of being accepted. The scoring system favours applicants who are graduate students and whose research areas are closer to the scope of the summer school. It also favours applicants who are from a Canadian university, an under-represented group, contribute to a research blog or an open-source project, those who have not attended the schools before, as well as students from research labs affiliated with the CIFAR program on Learning in Machines and Brains.