[w19/20] Deep Learning (Bischl)

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In recent years, deep learning has steadily increased in popularity, mainly due to their state-of-the-art performance in image and speech recognition, text mining and related tasks. Deep neural networks attempt to automatically learn multi-level representations and features of data and are able to uncover complex underlying data structures.

The lecture aims at providing a basic theoretical and practical understanding of modern neural network approaches. We will start out by covering the necessary background on traditional artificial neural networks, backpropagation, online learning and regularization. Then we will cover special methods used in deep learning, like drop-out and rectified linear units. We will also talk about further advanced topics like convolutional layers, recurrent neural networks and auto-encoders.