SoSe 18: Deep Learning
Christoph Wehmeyer
Additional information / Pre-requisites
This lecture/lab course is suitable for Master students of Mathematics, Computer Science or Computational Sciences
Students of the Computational Sciences program can combine this lecture/lab course with 19234502 + 19234501 (Mathematical aspects in machine learning) to complete “complex algorithms A/B”
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Comments
Qualification objectives: The students have a basic understanding of algebraic and computational methods for deep neural networks, their application scope and can practically build and train them with state-of-the-art software tools. They are familiar with typical deep learning structures and understand the relationship to their shallow counterparts.
Content:
- Perceptron
- Multilayer neural network and universal represenation theorem
- Backpropagation
- Deep feedforward networks
- Convolutional Neural Networks
- Autoencoder versus principal component analysis
- Time-autoencoder versus time-lagged independent component analysis
- Generative networks: Variational Autoencoders and Adversarial Generative Networks
- Active learning
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Additional appointments
Thu, 2018-07-26 10:00 - 12:00Regular appointments