• A comprehensive overview of concepts & principles behind Machine Learning
• Exploration of real-world applications of Machine Learning
• Differentiating different ML 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
• Learn about AI weather models that can take it up with numerical weather prediction
- Limitations of CNNs
- Introduction of transformer architecture
- Alternative approaches for predicting continuous data with ML
- Overview of Probabilistic Deep Learning
- Introduction to Diffusion Models
- Examples from Weather and Climate Research
• Hands-on downscaling of multiple variables using a transformer architecture on the HEALPix sphere.
- Extension of Reconstructing missing climate data exercise
- Implementation of a simple Flow Matching Model
- Evaluation of ensemble predictions and model uncertainty
- Recap session for deep learning basics covered in the course
- Open Q&A
