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Deep Learning for Geographical Data
| Term | SPW | Type | Lecturer | Person in charge |
|---|---|---|---|---|
| 3rd. (WS) | 4 | Lecture Exercises | Prof. Dr.-Ing. Martin Kada | Prof. Dr.-Ing. Martin Kada |
Course content
- Introduction to artificial intelligence
- Artificial neural networks, linear classifier, the perceptron
- Connected neural networks, training by backpropagation, loss and activation functions
- Weight initialization and regularization, optimizers, hyperparameter tuning
- Multi-task learning
- Convolutional neural networks (CNN)
- Common network architectures and custom models
- Deep Learning on 3D data and aerial laser scanning point clouds
- Time series geographical data
- Recurrent neural networks (RNN)
- Attention mechanism
- Generative learning with Autoencoders and generative adversarial networks (GAN)
- Deep Learning libraries (e.g. TensorFlow, PyTorch)