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RTML Network Workshop Resources

Author: Dr. Rob Lyon

Email : robert.lyon@manchester.ac.uk

web : www.scienceguyrob.com

I've provided the following notebooks:

  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.

These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.

I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.

I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.

License

The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.

Acknowledgements

The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.

Change log

Initial upload.

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Resources for the first Radiotherapy Machine Learning (RTML) Network event.

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