DLRL Summer School
Presented by CIFAR and Amii in collaboration with Mila and the Vector Institute
About DLRL Summer School
Each year, the CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School brings together graduate students, post-docs, and professionals to explore the fundamentals, latest advancements, and real-world applications of deep learning and reinforcement learning.
This year, the program is co-hosted by Amii and takes place in Edmonton, Alberta.
Over 12 intensive days, participants will learn from Canada CIFAR AI Chairs and leading AI experts from industry and academia. The program covers the latest developments in machine learning machine learning, along with networking opportunities, social events, and a career fair.
About DLRL Summer School
Each year, the CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School brings together graduate students, post-docs, and professionals to explore the fundamentals, latest advancements, and real-world applications of deep learning and reinforcement learning.
This year, the program is hosted by Amii and takes place in Edmonton, Alberta.
Over 12 intensive days, participants will learn from Canada CIFAR AI Chairs and leading AI experts from industry and academia. The program covers the latest developments in machine learning machine learning, along with networking opportunities, social events, and a career fair.
DLRL Program
Learn from top AI researchers, including Amii Chief Scientific Advisor and recent Turing Award recipient, Richard S. Sutton.
Who Should Apply?
DLRL welcomes graduate students, postdocs, and early-career researchers from quantitative disciplines who have experience in machine learning, deep learning, or reinforcement learning. Participants from around the world are encouraged to apply, with a focus on including individuals from groups underrepresented in AI.
Important Dates:
Mar. 18: Applications Open
Apr. 15: Application Deadline
Apr. 30: Registration Deadline
July 21: School Starts
Aug. 1: School Ends
Learn more at: dlrl.ca