Both the DLSS and the RLSS are supported by the Canadian Institute For Advanced Research (CIFAR) and the Centre de Recherches Mathématiques.
Video lectures available here
Deep Learning Summer School:
Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
The Deep Learning Summer School (DLSS) is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.
This year’s edition of the DLSS is organized by Graham Taylor, Aaron Courville and Yoshua Bengio.
Reinforcement Learning Summer School:
In collaboration with DLSS we will hold the first edition of the Montreal Reinforcement Learning Summer School (RLSS). 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.
The school is intended for graduate students in Machine Learning and related fields. Participants should have advanced prior training in computer science and mathematics, and preference will be given to students from research labs affiliated with the CIFAR program on Learning in Machines and Brains. Representatives from under-represented groups are strongly encouraged to apply.
This year’s edition of the RLSS is organized by Joelle Pineau and Doina Precup.
Any questions should be relayed to Genevieve Fried at genevieve.fried@mail.mcgill.ca
