We mentioned the slides earlier this summer (they are featured here) and we now have the attendant videos ! Thank you to the organizers: Joelle Pineau and Doina Precup. Graham Taylor, Aaron Courvilleand Yoshua Bengio for making these available on the interwebs.

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.

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.

Deep Learning Summer School

- 1:26:30 Machine Learning Doina Precup
- 3:03:15 Neural Networks Hugo Larochelle
- 1:25:47 Recurrent Neural Networks (RNNs) Yoshua Bengio
- 1:30:25 Probabilistic numerics for deep learning Michael Osborne
- 1:18:03 Generative Models I Ian Goodfellow
- 34:51 Theano Pascal Lamblin
- 1:05:58 AI Impact on Jobs Michael Osborne
- 1:28:54 Introduction to CNNs Richard Zemel
- 1:28:22 Structured Models/Advanced Vision Raquel Urtasun
- 55:15 Torch/PyTorch Soumith Chintala
- 1:28:25 Generative Models II Aaron Courville
- 1:24:30 Natural Language Understanding Phil Blunsom
- 1:23:42 Natural Language Processing Phil Blunsom
- 15:25 Bayesian Hyper Networks David Scott Krueger
- 14:01 Gibs Net Alex Lamb
- 12:23 Pixel GAN autoencoder Alireza Makhzani
- 16:16 CRNN's Rémi Leblond, Jean-Baptiste Alayrac
- 1:23:34 Deep learning in the brain Blake Aaron Richards
- 1:32:38 Theoretical Neuroscience and Deep Learning Theory Surya Ganguli
- 1:23:14 Marrying Graphical Models & Deep Learning Max Welling
- 1:21:05 Learning to Learn Nando de Freitas
- 1:18:12 Automatic Differentiation Matthew James Johnson
- 1:30:25 Combining Graphical Models and Deep Learning Matthew James Johnson
- Domain Randomization for Cuboid Pose Estimation Jonathan Tremblay
- 15:48 tbd Rogers F. Silva
- 16:26 What Would Shannon Do? Bayesian Compression for DL Karen Ullrich
- 13:13 On the Expressive Efficiency of Overlapping Architectures of Deep Learning Or Sharir

Reinforcement Learning Summer School

- 1:29:32 Reinforcement Learning Joelle Pineau
- 1:28:26 Policy Search for RL Pieter Abbeel
- 1:26:24 TD Learning Richard S. Sutton
- 1:21:20 Deep Reinforcement Learning Hado van Hasselt
- 1:23:52 Deep Control Nando de Freitas
- 1:23:58 Theory of RL Csaba Szepesvári
- 1:29:02 Reinforcement Learning Satinder Singh
- 1:21:44 Safe RL Philip S. Thomas
- 43:54 Applications of bandits and recommendation systems Nicolas Le Roux
- 1:02:35 Cooperative Visual Dialogue with Deep RL Devi Parikh, Dhruv Batra

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