Introduction to Machine Learning for Climate Scientists

Europe/Berlin
Room 034 (DKRZ Main Building)

Room 034

DKRZ Main Building

DKRZ Main Building, Bundesstraße 45a, 20146 Hamburg
Adeniyi Mosaku (DKRZ), Etienne Plésiat, Harsh Grover, Johannes Meuer (DKRZ), Paul Keil
Description

Target group: beginner in ML with some Python experience

Deadline for registration: January 31 15:00 CET

Machine learning is becoming a popular method for climate scientists. While there are many tutorials and courses available, researchers often face challenges when applying the tutorial concepts to actual climate data, that can be quite different from standard machine learning datasets.

Therefore, we are offering an Introduction to Machine Learning for Climate Scientists at DKRZ. The course will be held in person, and the number of participants is limited to 20. 

The course will be held from March 4 13:00 to March 5 16:00 at DKRZ seminar room 034. Coffee breaks will be provided during the workshop.

Participants are required to have a working knowledge of Python.

Organized by

Caroline Arnold, Johannes Meuer, Étienne Plesiat

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Evaluation of the ML workshop
Contact
    • 1:00 PM 6:05 PM
      Day 1
      Conveners: Adeniyi Mosaku (DKRZ), Harsh Grover (DKRZ), Paul Keil (DKRZ / HEREON)
      • 1:00 PM
        Introduction to Machine Learning 1h 40m

        • 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

        Speaker: Adeniyi Mosaku (DKRZ)
      • 2:40 PM
        Coffee Break 20m
      • 3:00 PM
        Architectures and Applications 1h 30m

        • An overview of state of the art Machine Learning Methods
        • Examples from weather, climate and beyond

        Speaker: Paul Keil
      • 4:30 PM
        Explainable AI 30m

        • Introduction to Explainable AI
        • Importance of Explainability
        • Interpretability techniques and use cases

        Speaker: Harsh Grover
  • Tuesday, March 5
    • 9:00 AM 4:00 PM
      Day 2
      Conveners: Etienne Plésiat, Johannes Meuer (DKRZ)
      • 9:00 AM
        PyTorch applied to Climate Science 1h 30m

        • Setup of the accounts
        • Introduction to PyTorch with examples
        • Definition of the task
        • Creation of the training, validation and test datasets

        Speaker: Etienne Plésiat
      • 10:30 AM
        Coffee Break 15m
      • 10:45 AM
        PyTorch applied to Climate Science 1h 15m

        • Building the CNN
        • Training the model
        • Testing the model

        Speaker: Etienne Plésiat
      • 12:00 PM
        Lunch break 1h 30m
      • 1:30 PM
        Advanced ML use-case: Reconstructing missing climate data 2h

        • Create and modify inpainting CNN for reconstructing climate data
        • Train the model with different configurations
        • Validate the model on test data

        Speaker: Johannes Meuer (DKRZ)
      • 3:30 PM
        Closing remarks 30m