Deep Learning

In many real world Machine Learning tasks, in particular those with perceptual input, such as vision and speech, the mapping from raw data to the output is often a complicated function with many factors of variation. Prior to 2010, to achieve decent performance on such tasks, significant effort had to be put to engineer hand crafted features.

Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. This automatic feature learning has been demonstrated to uncover underlying structure in the data leading to state-of-the-art results in tasks in vision, speech and rapidly in other domains as well.

This course aims to cover the basics of Deep Learning and some of the underlying theory with a particular focus on supervised Deep Learning, with a good coverage of unsupervised methods.