• 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 Machine Learning Methods
• Examples from weather, climate and beyond
• Introduction to Explainable AI
• Importance of Explainability
• Interpretability techniques and use cases
• Setup of the accounts
• Introduction to PyTorch with examples
• Definition of the task
• Creation of the training, validation and test datasets
• Create and modify inpainting CNN for reconstructing climate data
• Train the model with different configurations
• Validate the model on test data
