Goals of the course: The students will achieve working knowledge and hands on experience with the modern methods of the fast growing field of artificial intelligence.
The module will cover the following topics:
- What does the Deep-Learning-revolution mean for science and industry?
- Which methods of machine learning exist and their relative pros and cons?
- For which user cases is the application of these methods useful?
- How do I build and train a neural network for a given problem?
- Different types of neural networks (CNN, RNN, …)
- Generative modelling (AE, VAE, GAN, …)
- Healthy scepticism: evaluation of performance and accuracy.
- Discussion on ethics and privacy of data.
Roadmap:
Statistical Analysis, Optimization, Machine learning , Neural Networks, Deep Learning, Tensorflow and Keras, Applications, Own project.
- Trainer/in: Olena Linnyk
- Trainer/in: Kristin Neufeld
- Trainer/in: David Schneider
- Trainer/in: Raphael Skibka
- Trainer/in: Patrick Stärke
- Trainer/in: Kai Zhou