Overview

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.

This summer schools 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.

The summer school is organized within the Thematic Program on Statistical Inference, Learning, and Models for Big Data at Fields Institute.