• A comprehensive overview of the concepts and principle behind Machine Learning
• Exploration of real-world applications of Machine Learning
• Differentiating different Machine Learning types
• Introducing popular Machine Learning tools and frameworks
• An overview of state of the art Deep Learning Methods
• Examples from weather, climate and beyond
• Explainable AI
• Preparation of the DKRZ accounts for the Pytorch tutorials
• Brief theoretical introduction to PyTorch
• Hands-on illustrating the core concepts
• Definition of the task
• Creation of the training, validation and test datasets
• Building the model
• Training the model
• Testing the model
- Learn the basics of the deep learning framework Pytorch Lightning
- Write deep learning code that is flexible and performant
• Design and implement inpainting CNN for reconstructing climate data
• Train the model with different configurations
• Validate the model on test data
- Some snacks
- Some open chats about the day’s topics
• Learn about advanced spatial ML methods (Transformers, Graph Neural Networks) on the sphere
• Learn about AI weather models that can take it up with numerical weather prediction
